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6 Types of Filters in Tableau: How You Should Use Them

Updated on 10 July, 2024

69.37K+ views
13 min read

Tableau is one of the most popular tools in data visualization and analysis that facilitates brands across all domains to leverage the reckoning potential of acquiring Business Intelligence. For its seamless capability to yield readable insights and simplified dashboards, tableau has been instrumental for even non-technical subscribers to have access to personalized datasheets.

Tableau Filters benefit organizations because they help them present insightful data to clients and business stakeholders. This data is presented in the form of a worksheet or a dashboard. This facilitates better decision-making in business. The tableau filters can filter out sensitive data and share it only with those with access authority.

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There are different types of filters in a tableau that can be used to organize data based on predefined conditions and use them for data visualization. Such ability to filter large data sets in the Business Intelligence tool helps prepare for analysis, including removing irrelevant data records, reducing data sizes for faster processing, and more. The filters are required to highlight any underlying insights that can be derived from the data upon visualizing in a readable, actionable format. Check out our data science courses to learn more about data visualization.

You can go through the different types of Tableau filters discussed below if you are confused about which of the following are appropriate uses for filters in tableau? select all that apply.

What are Filters in Tableau?

Filters in Tableau are essential tools that allow users to refine and control the data displayed in visualizations. Serving as a means of data subset selection, different types of filters in Tableau are primarily used to focus on specific portions of the dataset based on predefined conditions or criteria. These conditions can be applied at different levels, such as the data source, worksheet, or dashboard. 

Filter types in Tableau play a pivotal role in enhancing the analytical process by enabling users to isolate relevant information, exclude unnecessary data, and tailor visualizations to specific subsets. They contribute to creating more meaningful and targeted insights by facilitating dynamic adjustments to the displayed data. 

Whether it’s restricting the timeframe through relative date filters, emphasizing top performers, or refining data based on custom conditions, Tableau filters empower users to interactively explore and analyze datasets, fostering a more efficient and tailored data visualization experience.

Why Do We Perform Filtering in Tableau?

Filtering in Tableau serves several important purposes, contributing to the overall effectiveness of data analysis and visualization. Here are some key reasons why filtering is performed in Tableau-

  • Focus on relevant data: Filtering allows users to concentrate on specific subsets of data that are relevant to their analysis. By excluding unnecessary information, users can focus on the key aspects of their dataset, making it easier to identify patterns and insights.
  • Enhance data exploration: Filters provide an interactive way to explore and interact with data. Users can dynamically adjust the displayed information, drill down into details, and gain a deeper understanding of the dataset by isolating specific dimensions or measures.
  • Improve performance: Applying filters can significantly enhance performance by reducing the amount of data loaded and processed. This is particularly important when dealing with large datasets, as filters help optimize the speed of visualizations and dashboards.
  • Tailor visualizations: Filters allow users to customize visualizations based on specific criteria. Whether it’s highlighting top-performing items, focusing on a particular time period, or isolating specific categories, filters help create more targeted and meaningful visualizations.
  • Support comparative analysis: Filtering enables users to conduct comparative analysis by selectively including or excluding data points. This is useful for scenarios such as comparing performance across regions, products, or time periods, providing valuable insights into trends and variations.
  • Simplify dashboards: Filters play a key role in simplifying dashboards by offering users the ability to control what they see. This helps in creating clean and concise visualizations that communicate insights effectively without overwhelming the audience with unnecessary details.
  • Dynamic time analysis: Filters like relative date filters in Tableau allow for dynamic time analysis. Users can easily switch between different time periods, compare trends, and analyze changes over time without manually adjusting date ranges.
  • Interactivity for users: Interactive dashboards with filters empower end-users to explore data on their terms. Quick filters and other interactive elements provide a user-friendly experience, allowing individuals to tailor the visualization to their specific needs and questions.

How Many Types of Filters Are There in Tableau?

There are six types of filters in Tableau that users can apply to their data to refine and customize their visualizations. These filter types in Tableau collectively provide users with a powerful set of tools to refine and analyze data in Tableau, offering flexibility and interactivity in exploring insights from their datasets. They are:

  • Extract Filters
  • Context Filters
  • Data Source Filters
  • Measure Filters
  • Dimension Filters
  • Table Filters

Before going into detail about each filter, here’s a brief overview of what each of the 6 filters listed above offer to users. 

Extract Filters enable users to limit the data extracted from the original source, optimizing performance. Context Filters in Tableau help prioritize and limit data by creating subsets that subsequent filters will consider.  Tableau’s Data Source Filters operate at the source level, affecting the entire workbook by restricting the data available for analysis.

Measure Filter in Tableau, on the other hand, enable the filtration of data based on specific measures, offering flexibility in analyzing numerical aspects. The Dimension Filter in Tableau allow users to filter data based on specific dimensions, refining the focus of visualizations.

Lastly, Table Filters provide an interactive way to filter data directly within a table, allowing for a more detailed examination of specific elements. 

Different Types of Filters in Tableau

Filters are a smart way to collate and segregate data based on its dimensions and sets to reduce the overall data frequency for faster processing. There are six different types of filters in tableau desktop based on their various objectives and are mentioned below as per their execution steps.

1. Extract Filters

As understood by its name, the extract filters are used to extract data from the various sources, by saving a screengrab of the way it gets added on your file. Such methods can help in lowering the tableau queries to the data source. As soon as you are done extracting data into your dashboard, you can create the extract and execute Hide All Unused Files to clear the columns unused in the datasheet of your panel. 

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The extract filter in Tableau extracts a tiny subset of data from the primary data source. Subsequently, Tableau creates the data set’s local copy, which will be saved in the repository.

This filter allows you to save a screenshot of how it appears in your workbook. The corresponding methods decrease Tableau queries. The data size can be decreased even more by implementing the dimension filter or measuring the extract as needed.

 

The steps to create an Extract filter in Tableau:

  1. Connect Tableau with the text file.
  2. Click on the “Extract button”. It will create a local copy in the Tableau source.

iii. Select the “Edit” option from the drop-down menu close to the Extract button in the upper right corner. It will open the Extract Data window. Now select the “Add” option in the Window.

  1. In this step, you need to select a Tableau filter condition from the “Add Filter” window. You can add any of the displayed fields as an Extract filter. Now select the category from the list and then click “OK”.
  2. A filtered window will be shown. It depicts data that was extracted through the Extract Tableau filter. You can customize the list or use all values within the list.

The extract filter is one of the versatile tableau filters. This is because it presents various options in addition to the general category to extract data. For example, the Wildcard option helps you to filter fields through a Wildcard match. It allows users to type the character, and the field will be filtered as per the match. The various types of matches are:

  1. Contains: Select the members if the member name comprises typed characters.
  2. Starts with: Select the members if the member name begins with typed characters.

iii. Ends with: Select the members if the member name terminates with typed characters.

Exactly matches: Select the members if the member name precisely matches with typed characters. 

These matches help you to customize your data and finally provide you with filtered data.

 You can filter data using various Byfield conditions after implementing the below steps:

  1. Select the “Condition” tab in the Filter window.
  2. Click on the “Byfield” button.

iii. Select the name of the field you want to filter.

  1. Now select the aggregation type like average, sum, and median from the drop-down list.
  2. Select an operator from the drop-down list.
  3. Enter the value to filter the selected field.

vii. Click “OK”.

2. Data Source Filter

Used mainly to restrict sensitive data from the data viewers, the data source filters are similar to the extract filters in minimizing the data feeds for faster processing. 

The data source filter in tableau helps in the direct application of the filter environment to the source data and quickly uploads data that qualifies the scenario into the tableau workbook. To execute such processes, you need to go to the Data Source tab and select the Add option in the upper right corner. 

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Clicking on the Add option in the menu would open into a dialog box, where you can select the field and choose through the values you want to record. Once you press confirmation, you shall be presented with a summary of the presets selected from the data source filters. 

3. Context Filter

A context filter is a discrete filter on its own, creating datasets based on the original datasheet and the presets chosen for compiling the data. Since all the types of filters in tableau get applied to all rows in the datasheet, irrespective of any other filters, the context filter would ensure that it is first to get processed.

Despite being constrained to view all data rows, it can be implemented to choose sheets as and when required to optimize its performance by minimizing the data efficiently. 

The context filter helps in applying a relevant, actionable context to the entire data analysis in tableau. If there are multiple filter preset categories used in the worksheet, dividing it into many parts can overall turn into a context filter in itself that guides all the other filters present in the datasheet. 

It helps you to generate data sets by employing appropriate presets for compilation. It is always processed first, although other tableau filters are used. The multiple preset categories existing in the worksheet can be categorized into several parts that would work like a context filter. The data sets are generated according to the original datasheet. Moreover, the data can be efficiently minimized to allow viewing of all data rows, notwithstanding the constraints. You can choose the sheets when required.

You need to open the Context menu of a prevailing categorical filter and choose “Add to Context”. The context is calculated after the view is created. Subsequently, the context is used to count all other filters.

Go to the context menu of a prevailing categorical filter and select Add to Context to make a Context filter in tableau. Once the view is created, the context is calculated. The context is then used to calculate all other filters. You can use Context Filter to find the topmost 10 subcategories of items in the Furniture category.

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4. Dimension filter

Now that you’ve chosen the data, you can access the values highlighted or remove them from the selected dimension, represented as strikethrough values. You can click All or None to select or deselect based on your operation in case of multiple dimensions. 

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5. Measure Filters

In this filter, you can apply the various operations like Sum, Avg, Median, Standard Deviation, and other aggregate functions. In the next stage, you would be presented with four choices: Range, At least, At most, and Special for your values. Every time you drag the data you want to filter, you can do that in a specific setting. 

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Aggregated filters are always employed after non-aggregated filters, irrespective of the order present on the Filters pane. Measure filters are used to measure fields comprising quantitative data.

Measure filter in tableau shows four types of filters, as mentioned below.

  • Range: Selects the range of values to incorporate into the result. This could include specifying a minimum and maximum value to include in the result set. For instance, if you’re analyzing sales data, you might use the Range filter to focus on a specific sales range, such as values between $100,000 and $500,000.
  • At least: Selects a measure’s minimum value of. This filter is particularly useful when you want to emphasize a lower limit for a measure. For example, you might use “At Least” to analyze products with sales exceeding a certain threshold.
  • At most: Selects a measure’s maximum value. This filter is beneficial when you want to emphasize an upper limit for a measure. For instance, you might use “At Most” to focus on customers with a purchase history below a certain total.
  • Special: Selects null or non-null values. This can be useful when dealing with missing data or when you specifically want to analyze data points with null values. For instance, you might use the Special filter to isolate and investigate cases where certain measures are not available.

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6. Table Filters

The last filter to process is the table calculation that gets executed once the data view has been rendered. With this filter, you can quickly look into the data without any filtering of the hidden data. 

Unlike other types of filters in Tableau applied during the data processing phase, the table calculation filter operates post-rendering, allowing users to interact with the data without directly affecting the underlying dataset.

When you apply a table calculation filter, you essentially perform calculations on the displayed results rather than the raw data itself. This allows for dynamic and interactive exploration of the data without applying permanent changes to the dataset. Users can quickly analyze trends, patterns, or outliers without altering the original data view or applying restrictions to hidden data.

This type of filter is particularly valuable for on-the-fly analysis and exploration. It provides a real-time, responsive way to manipulate visualizations without committing to permanent changes in the data. For example, you might apply a table calculation filter to dynamically compute moving averages, percent changes, or other aggregations based on the current display, offering a more interactive and exploratory data analysis experience.

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In addition to these six major types of filters in Tableau, you may have to use other types of filters. Some of them are discussed below.

Quick filter:

Quick Filters provide a swift and accessible means to implement various filter types in Tableau. By simply right-clicking on a field, users can efficiently access different filtering options. It helps you to quickly access different filter types in Tableau through the right-click option. It owns features required to fulfill all typical filtering needs. You can implement Quick filters in Tableau on measures or dimensions.

This feature is particularly handy for users who want a fast and uncomplicated way to interactively explore and analyze their data.

Global filter:

It can be used over multiple worksheets by using the same source data in a workbook. Moreover, it can be used on all worksheets by utilizing the same data. This ensures consistency in data representation throughout various sheets. Changes made to the filter criteria in one place automatically propagate to affect all related sheets, promoting coherence and efficiency in data analysis.

Global Filters are valuable for maintaining uniformity and avoiding discrepancies in data visualization across different parts of a workbook.

 Cascading filter:

It allows for the selections in the first filter to modify the options in the second filter. So, it restricts the values to those that are only significant to the first filter. Moreover, it avoids users from choosing irrelevant data. Hence, it offers an improved user experience. In other words, the Cascading Filter helps users focus on specific subsets of data by tailoring available options based on their initial selections.

This functionality is particularly beneficial when dealing with large datasets or complex data structures, as it streamlines the process of narrowing down choices and avoids overwhelming users with irrelevant information.

 User filter:

Its alternate name is row-level security. This filter in tableau restricts and administers the data that users can view or access depending on the authority specified. Essentially, the User Filter allows administrators to define rules and permissions that determine which data rows individual users or groups are permitted to access. By associating specific users or groups with particular data filters, Tableau ensures that users only see the subset of data they are authorized to view.

This functionality is particularly crucial when dealing with confidential or sensitive information, as it enables organizations to implement strict access controls, aligning with privacy and compliance standards.

Conclusion

This is how different types of filters in tableau work in various processes.

Hence, the various types of filters in Tableau are like handy tools that play a crucial role in different parts of working with data. They help us dig out important insights, make our visualizations clearer, and focus on specific details. Whether we’re using context filters for a closer look or top N filters to narrow down our focus, Tableau gives us the freedom to adapt our approach.

By working with these different types of filters in Tableau, your data exploration can be smooth and interactive experience, making your analyses more efficient and impactful. These filters become our reliable companions as we navigate through the world of data, making our Tableau journey more insightful and enjoyable.

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Frequently Asked Questions (FAQs)

1. What are some of the basic filters in Tableau?

Filtering is the process of removing certain information from the available data by putting different filters. Tableau is the most widely used data visualization tool with plenty of features to simplify the process. Tableau provides both basic filters to deal with simple scenarios and advanced context-based filters for performing complex calculations. The three types of basic filters available in Tableau are: Filter Dates – This filter is applied on the date fields to remove specific date entries that are not required. Filter Measures – This filter is applied to the measure fields to remove specific measures based on the requirements. Filter Dimensions – This filter is applied on the dimension fields for removing certain measures that are not required for the calculation.

2. What is the difference between a normal filter and a quick filter in Tableau?

In Tableau, filters are useful for restricting the data from the database. There are different types of filters available in Tableau for performing different functions. Every filter has its own purpose and use, which makes it a worthy one in the list of available basic and advanced filters in tableau. A normal filter is useful for restricting the database's data based on the selected measure or dimension. This traditional filter can be created by simply dragging a field onto the shelf of filters. Quick filter helps us view all the filtering options and filter every worksheet on the dashboard by changing the values dynamically. This could be done even during the run time within the range that has been defined.

3. What are the different types of filters based on the purpose available in Tableau?

Tableau filters can be utilized for restricting the number of records that are present in a worksheet. Different types of filters are applied to a dataset based on the purpose and requirements. The filters are executed in a particular order for performing all the actions. The following is the list of the filters that are sorted in the order of their execution in Tableau: Extract filters Data Source filters Context filters Dimension filters Measure filters Every filter has its own purpose and is used for organizing and simplifying the available dataset in different ways through its application.

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Rohit Sharma

Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

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Announcing PG Diploma in Data Analytics with IIIT Bangalore

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Data is in abundance and for corporations, big or small, investment in data analytics is no more a discretionary spend, but a mandatory investment for competitive advantage. In fact, by 2019, 90% of large organizations will have a Chief Data Officer. Indian data analytics industry alone is expected to grow to $2.3 billion by 2017-18. UpGrad’s survey also shows that leaders across industries are looking at data as a key growth driver in the future and believe that the data analytics wave is here to stay. Learn Data Science Courses online at upGrad This growth wave has created a critical supply-demand imbalance of professionals with the adequate know-how of making data-driven decisions. The scarcity exists across Data Engineers, Data Analysts and becomes more acute when it comes to Data Scientists. As a result of this imbalance, India will face an acute shortage of at least 2 lac data skilled professionals over the next couple of years. upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 In pursuit of bridging this gap, UpGrad has partnered with IIIT Bangalore, to deliver a first-of-its-kind online PG Diploma program in Data Analytics, which over the years will train 10,000 professionals. Offering a perfect mix of academic rigor and industry relevance, the program is meant for all those working professionals who wish to accelerate their career in data analytics. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Top Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Programs Inferential Statistics Programs 2 Hypothesis Testing Programs Logistic Regression Programs 3 Linear Regression Programs Linear Algebra for Analysis Programs The Advanced Certificate Programme in Data Science at UpGrad will include modules in Statistics, Data Visualization & Business Intelligence, Predictive Modeling, Machine Learning, and Big Data. Additionally, the program will feature a 3-month project where students will work on real industry problems in a domain of their choice. The first batch of the program is scheduled to start on May 2016.   Explore our Popular Data Science Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Certifications Our learners also read: Learn Python Online Course Free
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by Rohit Sharma

08 Feb'16
How Organisations can Benefit from Bridging the Data Scientist Gap

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Note: The article was originally written for LinkedIn Pulse by Sameer Dhanrajani, Business Leader at Cognizant Technology Solutions. Data Scientist is one of the fastest-growing and highest paid jobs in technology industry. Dr. Tara Sinclair, Indeed.com’s chief economist, said the number of job postings for “data scientist” grew 57% year-over-year in Q1:2015. Yet, in spite of the incredibly high demand, it’s not entirely clear what education someone needs to land one of these coveted roles. Do you get a degree in data science? Attend a bootcamp? Take a few Udemy courses and jump in? Learn data science to gain edge over your competitors It depends on what practice you end up it. Data Sciences has become a widely implemented phenomenon and multiple companies are grappling to build a decent DS practice in-house. Usually online courses, MOOCs and free courseware usually provides the necessary direction for starters to get a clear understanding, quickly for execution. 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Every month, we hear a new course starting at a globally recognized university. Data growth is headed in one direction, so it’s clear that the skills gap is a long-term problem. But many businesses just can’t wait the three to five years it might take today’s undergrads to become business-savvy professionals. Hence this aptly briefs an alarming need of analytics education and why universities around the world are scrambling to get started on the route towards being analytics education leaders. Obviously, the first mover advantage would define the best courses in years to come i.e. institutes that take up the data science journey sooner would have a much mature footing in next few years and they would find it easier to attract and place students. Strategic Benefits to implementing Data Science Degrees Data science involves multiple disciplines The reason why data scientists are so highly sought after, is because the job is really a mashup of different skill sets and competencies rarely found together. Data scientists have tended to come from two different disciplines, computer science and statistics, but the best data science involves both disciplines. One of the dangers is statisticians not picking up on some of the new ideas that are coming out of machine learning, or computer scientists just not knowing enough classical statistics to know the pitfalls. Even though not everything can be taught in a Degree course, universities should clearly understand the fact that training a data science graduate would involve including multiple, heterogeneous skills as curriculum and not one consistent courseware. 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by Ashish Korukonda

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Computer Center turns Data Center; Computer Science turns Data Science

(This article, written by Prof. S. Sadagopan, was originally published in Analytics India Magazine) There is an old “theory” that talks of “power shift” from “carrier” to “content” and to “control” as industry matures. Here are some examples In the early days of Railways, “action” was in “building railroads”; the “tycoons” who made billions were those “railroad builders”. Once enough railroads were built, there was more action in building “engines and coaches” – General Electric and Bombardier emerged; “power” shifted from “carrier” to “content”; still later, action shifted to “passenger trains” and “freight trains” – AmTrak and Delhi Metro, for example, that used the rail infrastructure and available engines and coaches / wagons to offer a viable passenger / goods transportation service; power shifted from “content” to “control”. The story is no different in the case of automobiles; “carrier” road-building industry had the limelight for some years, then the car and truck manufacturers – “content” – GM, Daimler Chrysler, Tata, Ashok Leyland and Maruti emerged – and finally, the “control”, transport operators – KSRTC in Bangalore in the Bus segment to Uber and Ola in the Car segment. In fact, even in the airline industry, airports become the “carrier”, airplanes are the “content” and airlines represent the “control” Learn data science courses from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. It is a continuum; all three continue to be active – carrier, content and control – it is just the emphasis in terms of market and brand value of leading companies in that segment, profitability, employment generation and societal importance that shifts. We are witnessing a similar “power shift” in the computer industry. For nearly six decades the “action” has been on the “carrier”, namely, computers; processors, once proprietary from the likes of IBM and Control Data, then to microprocessors, then to full blown systems built around such processors – mainframes, mini computers, micro computers, personal computers and in recent times smartphones and Tablet computers. Intel and AMD in processors and IBM, DEC, HP and Sun dominated the scene in these decades. A quiet shift happened with the arrival of “independent” software companies – Microsoft and Adobe, for example and software services companies like TCS and Infosys. Along with such software products and software services companies came the Internet / e-Commerce companies – Yahoo, Google, Amazon and Flipkart; shifting the power from “carrier” to “content”. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses This shift was once again captured by the use of “data center” starting with the arrival of Internet companies and the dot-com bubble in late nineties. In recent times, the term “cloud data center” is gaining currency after the arrival of “cloud computing”. Though interest in computers started in early fifties, Computer Science took shape only in seventies; IITs in India created the first undergraduate program in Computer Science and a formal academic entity in seventies. In the next four decades Computer Science has become a dominant academic discipline attracting the best of the talent, more so in countries like India. With its success in software services (with $ 160 Billion annual revenue, about 5 million direct jobs created in the past 20 years and nearly 7% of India’s GDP), Computer Science has become an aspiration for hundreds of millions of Indians. With the shift in “power” from “computers” to “data” – “carrier” to “content” – it is but natural, that emphasis shifts from “computer science” to “data science” – a term that is in wide circulation only in the past couple of years, more in corporate circles than in academic institutions. In many places including IIIT Bangalore, the erstwhile Database and Information Systems groups are getting re-christened as “Data Science” groups; of course, for many acdemics, “Data Science” is just a buzzword, that will go “out of fashion” soon. Only time will tell! As far as we are concerned, the arrival of data science represents the natural progression of “analytics”, that will use the “data” to create value, the same way Metro is creating value out of railroad and train coaches or Uber is creating value out of investments in road and cars or Singapore Airlines creating value out of airport infrastructure and Boeing / Airbus planes. More important, the shift from “carrier” to “content” to “control” also presents economic opportunities that are much larger in size. We do expect the same from Analytics as the emphasis shifts from Computer Science to Data Science to Analytics. 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Our learners also read: Free Online Python Course for Beginners About Prof. S. Sadagopan Professor Sadagopan, currently the Director (President) of IIIT-Bangalore (a PhD granting University), has over 25 years of experience in Operations Research, Decision Theory, Multi-criteria optimization, Simulation, Enterprise computing etc. His research work has appeared in several international journals including IEEE Transactions, European J of Operational Research, J of Optimization Theory & Applications, Naval Research Logistics, Simulation and Decision Support Systems. He is a referee for several journals and serves on the editorial boards of many journals.
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by Prof. S. Sadagopan

11 May'16
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Note: The articlewas originally written by Sameer Dhanrajani, Business Leader at Cognizant Technology Solutions. A Better Talent acquisition Framework Although many articles have been written lamenting the current talent shortage in analytics and data science, I still find that the majority of companies could improve their success by simply revamping their current talent acquisition processes. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. We’re all well aware that strong quantitative professionals are few and far between, so it’s in a company’s best interest to be doing everything in their power to land qualified candidates as soon as they find them. It’s a candidate’s market, with strong candidates going on and off the market lightning fast, yet many organizational processes are still slow and outdated. These sluggish procedures are not equipped to handle many candidates who are fielding multiple offers from other companies who are just as hungry (if not more so) for quantitative talent. Here are the key areas I would change to make hiring processes more competitive: Fix your salary bands – It (almost) goes without saying that if your salary offerings are outdated or aren’t competitive to the field, it will be difficult for you to get the attention of qualified candidates; stay topical with relevant compensation grids. Consider one-time bonuses – Want to make your offer compelling but can’t change the salary? Sign-on bonuses and relocation packages are also frequently used, especially near the end of the year, when a candidate is potentially walking away from an earned bonus; a sign-on bonus can help seal the deal. Be open to other forms of compensation – There are plenty of non-monetary ways to entice Quants to your company, like having the latest tools, solving challenging problems, organization-wide buy-in for analytics and more. Other things to consider could be flexible work arrangements, remote options or other unique perks. Pick up the pace – Talented analytics professionals are rare, and the chances that qualified candidates will be interviewing with multiple companies are very high. Don’t hesitate to make an offer if you find what you’re looking for at a swift pace – your competitors won’t. Court the candidate – Just as you want a candidate who stands out from the pack, a candidate wants a company that makes an effort to stand apart also. I read somewhere, a client from Chicago sent an interviewing candidate and his family pizzas from a particularly tasty restaurant in the city. I can’t say for sure that the pizza was what persuaded him to take the company’s offer, but a little old-fashioned wooing never hurts. Button up the process – Just as it helps to have an expedited process, it also works to your benefit is the process is as smooth and trouble-free as you can make it. This means hassle-free travel arrangements, on-time interviews, and quick feedback. Network – make sure that you know the best of the talent available in the market at all levels and keep in touch with them thru porfessional social sites on subtle basis as this will come handy in picking the right candidate on selective basis Redesigned Interview Process In the old days one would screen resumes and then schedule lots of 1:1’s. Typically people would ask questions aimed at assessing a candidate’s proficiency with stats, technicality, and ability to solve problems. But there were three problems with this – the interviews weren’t coordinated well enough to get a holistic view of the candidate, we were never really sure if their answers would translate to effective performance on the job, and from the perspective of the candidate it was a pretty lengthy interrogation. So, a new interview process need to be designed that is much more effective and transparent – we want to give the candidate a sense for what a day in the life of a member on the team is like, and get a read on what it would be like to work with a company. In total it takes about two days to make a decision, and there be no false positives (possibly some false negatives though), and the feedback from both the candidates and the team members has been positive. There are four steps to the process: Resume/phone screens – look for people who have experience using data to drive decisions, and some knowledge of what your company is all about. On both counts you’ll get a much deeper read later in the process; you just want to make sure that moving forward is a good use of either of both of your time. Basic data challenge – The goal here is to validate the candidate’s ability to work with data, as described in their resume. So send a few data sets to them and ask a basic question; the exercise should be easy for anyone who has experience. In-house data challenge – This is should be the meat of the interview process. Try to be as transparent about it as possible – they’ll get to see what it’s like working with you and vice versa. So have the candidate sit with the team, give them access to your data, and a broad question. They then have the day to attack the problem however they’re inclined, with the support of the people around them. Do encourage questions, have lunch with them to ease the tension, and check-in periodically to make sure they aren’t stuck on something trivial. 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In many instances, these are very complex algorithms and very technical findings. However, a data scientist is only as good as the person he must relay his findings to. Others within the business need to be able to understand this information and apply these insights appropriately. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Good data scientists can make analogies and metaphors to explain the data but not every concept can be boiled down in layman’s terms. A space rocket is not an automobile and, in the brave new world, everyone must make this paradigm shift. Top Data Science Skills You Should Learn SL. 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Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Our learners also read: Free Python Course with Certification And lastly, the data scientist you’re looking for needs to have strong business acumen. Do they know your business? Do they know what problems you’re trying to solve? And do they find opportunities that you never would have guessed or spotted?
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by upGrad

14 May'16
UpGrad partners with Analytics Vidhya

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UpGrad partners with Analytics Vidhya

We are happy to announce our partnership with Analytics Vidhya, a pioneer in the Data Science community. Analytics Vidhya is well known for its impressive knowledge base, be it the hackathons they organize or tools and frameworks that they help demystify. In their own words, “Analytics Vidhya is a passionate community for Analytics/Data Science professionals, and aims at bringing together influencers and learners to augment knowledge”. Explore our Popular Data Science Degrees Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Degrees We are joining hands to provide candidates of our PG Diploma in Data Analytics, an added exposure to UpGrad Industry Projects. While the program already covers multiple case studies and projects in the core curriculum, these projects with Analytics Vidhya will be optional for students to help them further hone their skills on data-driven problem-solving techniques. To further facilitate the learning, Analytics Vidhya will also be providing mentoring sessions to help our students with the approach to these projects. Our learners also read: Free Online Python Course for Beginners Top Essential Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Certifications Inferential Statistics Certifications 2 Hypothesis Testing Certifications Logistic Regression Certifications 3 Linear Regression Certifications Linear Algebra for Analysis Certifications This collaboration brings great value to the program by allowing our students to add another dimension to their resume which goes beyond the capstone projects and case studies that are already a part of the program. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Through this, we hope our students would be equipped to showcase their ability to dissect any problem statement and interpret what the model results mean for business decision making. This also helps us to differentiate UpGrad-IIITB students in the eyes of the recruiters. upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Check out our data science training to upskill yourself
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by Omkar Pradhan

09 Oct'16
Data Analytics Student Speak: Story of Thulasiram

5.69K+

Data Analytics Student Speak: Story of Thulasiram

When Thulasiram enrolled in the UpGrad Data Analytics program, in its first cohort, he was not very different for us, from the rest of our students in this. While we still do not and should not treat learners differently, being in the business of education – we definitely see this particular student in a different light. His sheer resilience and passion for learning shaped his success story at UpGrad. Humble beginnings Born in the small town of Chittoor, Andhra Pradesh, Thulasiram does not remember much of his childhood given that he enlisted in the Navy at a very young age of about 15 years. Right out of 10th standard, he trained for four years, acquiring a diploma in mechanical engineering. Thulasiram came from humble means. His father was the manager of a small general store and his mother a housewife. It’s difficult to dream big when leading a sheltered life with not many avenues for exposure to unconventional and exciting opportunities. But you can’t take learning out of the learner. “One thing I remember about school is our Math teacher,” reminisces Thulasiram, “He used to give us lot of puzzles to solve. I still remember one puzzle. If you take a chessboard and assume that all pawns are queens; you have to arrange them in such a way that none of the eight pawns should die. Every queen, should not affect another queen. It was a challenging task, but ultimately we did it, we solved it.” Navy & MBA At 35 years of age, Thulasiram has been in the navy for 19 years. Presently, he is an instructor at the Naval Institute of Aeronautical Technology. “I am from the navy and a lot of people don’t know that there is an aviation wing too. So, it’s like a dream; when you are a small child, you never dream of touching an aircraft, let alone maintaining it. I am very proud of doing this,” says Thulasiram on taking the initiative to upskill himself and becoming a naval-aeronautics instructor. When the system doesn’t push you, you have to take the initiative yourself. Thulasiram imbibed this attitude. He went on to enroll in an MBA program and believes that the program drastically helped improve his communication skills and plan his work better. How Can You Transition to Data Analytics? Data Analytics Like most of us, Thulasiram began hearing about the hugely popular and rapidly growing domain of data analytics all around him. Already equipped with the DNA of an avid learner and keen to pick up yet another skill, Thulasiram began researching the subject. He soon realised that this was going to be a task more rigorous and challenging than any he had faced so far. It seemed you had to be a computer God, equipped with analytical, mathematical, statistical and programming skills as prerequisites – a list that could deter even the most motivated individuals. This is where Thulsiram’s determination set him apart from most others. Despite his friends, colleagues and others that he ran the idea by, expressing apprehension and deterring him from undertaking such a program purely with his interests in mind – time was taken, difficulty level, etc. – Thulasiram, true to the spirit, decided to pursue it anyway. Referring to the crucial moment when he made the decision, he says, If it is easy, everybody will do it. So, there is no fun in doing something which everybody can do. I thought, let’s go for it. Let me push myself — challenge myself. Maybe, it will be a good challenge. Let’s go ahead and see whether I will be able to do it or not. UpGrad Having made up his mind, Thulasiram got straight down to work. After some online research, he decided that UpGrad’s Data Analytics program, offered in collaboration with IIIT-Bangalore that awarded a PG Diploma on successful completion, was the way to go. The experience, he says, has been nothing short of phenomenal. It is thrilling to pick up complex concepts like machine learning, programming, or statistics within a matter of three to four months – a feat he deems nearly impossible had the source or provider been one other than UpGrad. Our learners also read: Top Python Free Courses Favorite Elements Ask him what are the top two attractions for him in this program and, surprising us, he says deadlines! Deadlines and assignments. He feels that deadlines add the right amount of pressure he needs to push himself forward and manage time well. As far as assignments are concerned, Thulasiram’s views resonate with our own – that real-life case studies and application-based learning goes a long way. Working on such cases and seeing results is far superior to only theoretical learning. He adds, “flexibility is required because mostly only working professionals will be opting for this course. You can’t say that today you are free, because tomorrow some project may be landing in your hands. So, if there is no flexibility, it will be very difficult. With flexibility, we can plan things and maybe accordingly adjust work and family and studies,” giving the UpGrad mode of learning, yet another thumbs-up. Amongst many other great things he had to say, Thulasiram was surprised at the number of live sessions conducted with industry professionals/mentors every week. Along with the rest of his class, he particularly liked the one conducted by Mr. Anand from Gramener. Top Data Science Skills to Learn to upskill SL. No Top Data Science Skills to Learn 1 Data Analysis Online Courses Inferential Statistics Online Courses 2 Hypothesis Testing Online Courses Logistic Regression Online Courses 3 Linear Regression Courses Linear Algebra for Analysis Online Courses What Kind of Salaries do Data Scientists and Analysts Demand? Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4 Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses “Have learned most here, only want to learn..” Interested only in learning, Thulasiram made this observation about the program – compared to his MBA or any other stage of life. He signs off calling it a game-changer and giving a strong recommendation to UpGrad’s Data Analytics program. We are truly grateful to Thulasiram and our entire student community who give us the zeal to move forward every day, with testimonials like these, and make the learning experience more authentic, engaging, and truly rewarding for each one of them. If you are curious to learn about data analytics, data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
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by Apoorva Shankar

07 Dec'16
Decoding Easy vs. Not-So-Easy Data Analytics

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Decoding Easy vs. Not-So-Easy Data Analytics

Authored by Professor S. Sadagopan, Director – IIIT Bangalore. Prof. Sadagopan is one of the most experienced academicians on the expert panel of UpGrad & IIIT-B PG Diploma Program in Data Analytics. As a budding analytics professional confounded by jargon, hype and overwhelming marketing messages that talk of millions of upcoming jobs that are paid in millions of Rupees, you ought to get clarity about the “real” value of a data analytics education. Here are some tidbits – that should hopefully help in reducing your confusion. Some smart people can use “analytical thinking” to come up with “amazing numbers”; they are very useful but being “intuitive”, they cannot be “taught.” For example: Easy Analytics Pre-configuring ATMs with Data Insights  “We have the fastest ATM on this planet” Claimed a respected Bank. Did they get a new ATM made especially for them? No way. Some smart employee with an analytical mindset found that 90% of the time that users go to an ATM to withdraw cash, they use a fixed amount, say Rs 5,000. So, the Bank re-configured the standard screen options – Balance Inquiry, Withdrawal, Print Statement etc. – to include another option. Withdraw XYZ amount, based on individual customer’s past actions. This ended up saving one step of ATM operation. Instead of selecting the withdrawal option and then entering the amount to be withdrawn, you could now save some time – making the process more convenient and intuitive. A smart move indeed, however, this is something known as “Easy Analytics” that others can also copy. In fact, others DID copy, within three months! A Start-Up’s Guide to Data Analytics Hidden Data in the Weather In the sample data-sets that used to accompany a spreadsheet product in the 90’s, there used to be data on the area and population of every State in the United States. There was also an exercise to teach the formula part of the spreadsheet to compute the population density (population per sq. km). New Jersey, with a population of 467 per sq. km, is the State with the highest density. While teaching a class of MBA students in New Jersey, I met an Indian student who figured out that in terms of population density, New Jersey is more crowded than India with 446 people per sq. km!  An interesting observation, although comparing a State with a Country is a bit misleading. Once again, an Easy Analytics exercise leading to a “nice” observation! Some simple data analytics exercises can be routinely done, and are made relatively easier, thanks to amazing tools: B-School Buying Behavior Decoded In a B-School in India that has a store on campus, (campus is located far from the city center) some smart students put several years of sales data of their campus store. They were excited by the phenomenal computer power and near, idiot-proof analytics software. The real surprise, however, was that eight items accounted for 85% of their annual sales. More importantly, these eight items were consumed in just six days of the year! Everyone knew that a handful of items were the only fast-moving items, but they did not know the extent (85%) or the intensity (consumption in just six days) of this. It turns out that in the first 3 days of the semester the students would stock the items for the full semester! The B-School found it sensible to request a nearby store to prop up a temporary stall for just two weeks at the beginning of the semesters and close down the Campus Store. This saved useful space and costs without causing major inconvenience to the students. A good example of Easy Analytics done with the help of a powerful tool. Top 4 Data Analytics Skills You Need to Become an Expert! The “Not So Easy” Analytics needs deep analytical understanding, tools, an ‘analytical mindset’ and some hard work. Here are two examples, one taken from way back in the 70’s and the other occurring very recently: Not-So-Easy Analytics To Fly or Not to Fly, That is the Question Long ago, the American Airlines perfected planned overbooking of airline seats, thanks to SABRE Airline Reservation system that managed every airline seat. Armed with detailed past data of ‘empty seats’ and ‘no show’ in every segment of every flight for every day through the year, and modeling airline seats as perishable commodities, the American Airlines was able to improve yield, i.e., utilization of airplane capacity. They did this through planned overbooking – selling more tickets than the number of seats, based on projected cancellations. Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications If indeed more passengers showed up than the actual number of seats, American Airlines would request anyone volunteering to forego travel in the specific flight, with the offer to fly them by the next flight (often free) and taking care of hotel accommodation if needed. Sometimes, they would even offer cash incentives to the volunteer to opt-out. Using sophisticated Statistical and Operational Research modeling, American Airlines would ensure that the flights went full and the actual incidents of more passengers than the full capacity, was near zero. In fact, many students would look forward to such incidents so that they could get incentives, (in fact, I would have to include myself in this list) but rarely were they rewarded!) upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 What American Airlines started as an experiment has become the standard industry practice over the years. Until recently, a team of well-trained (often Ph.D. degree holders) analysts armed with access to enormous computing power, was needed for such an analytics exercise to be sustained. Now, new generation software such as the R Programming language and powerful desktop computers with significant visualization/graphics power is changing the world of data analytics really fast. Anyone who is well-trained (not necessarily requiring a Ph.D. anymore) can become a first-rate analytics professional. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification Unleashing the Power of Data Analytics Our learners also read: Free Python Course with Certification Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences?   Cab Out of the Bag Uber is yet another example displaying how the power of data analytics can disrupt a well-established industry. Taxi-for-sure in Bangalore and Ola Cabs are similar to Uber. Together, these Taxi-App companies (using a Mobile App to hail a taxi, the status monitor the taxi, use and pay for the taxi) are trying to convince the world to move from car ownership to on-demand car usage. A simple but deep analytics exercise in the year 2008 gave such confidence to Uber that it began talking of reducing car sales by 25% by the year 2025! After building the Uber App for iPhone, the Uber founder enrolled few hundreds of taxi customers in San Francisco and few hundreds of taxi drivers in that area as well. All that the enrolled drivers had to do was to touch the Uber App whenever they were ready for a customer. Similarly, the enrolled taxi customers were requested to touch the Uber App whenever they were looking for a taxi. Thanks to the internet-connected phone (connectivity), Mobile App (user interface), GPS (taxi and end-user location) and GIS (location details), Uber could try connecting the taxi drivers and the taxi users. The real insight was that nearly 90% of the time, taxi drivers found a customer, less than 100 meters away! In the same way, nearly 90% of the time, taxi users were connected with their potential drivers in no time, not too far away. Unfortunately, till the Uber App came into existence, riders and taxi drivers had no way of knowing this information. More importantly, they both had no way of reaching each other! Once they had this information and access, a new way of taxi-hailing could be established. With back-end software to schedule taxis, payment gateway and a mobile payment mechanism, a far more superior taxi service could be established. Of course, near home, we had even better options like Taxi-for-sure trying to extend this experience even to auto rickshaws. The rest, as they say, is “history in the making!” Deep dive courses in data analytics will help prepare you for such high impact applications. It is not easy, but do remember former US President Kennedy’s words “we chose to go to the Moon not because it is easy, but because it is hard!” Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.  
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by Prof. S. Sadagopan

14 Dec'16
Launching UpGrad’s Data Analytics Roadshow – Are You Game?

5.14K+

Launching UpGrad’s Data Analytics Roadshow – Are You Game?

We, at UpGrad, are excited to announce a brand new partnership with various thought leaders in the Data Analytics industry – IIIT Bangalore, Genpact, Analytics Vidhya and Gramener – to bring to you a one-of-a-kind Analytics Roadshow! As part of this roadshow, we will be conducting several back-to-back events that focus on different aspects of analytics, creating interaction points across India, to do our bit for a future ready and analytical, young workforce.  Also Read: Analytics Vidhya article on the UpGrad Data Analytics Roadshow Here is the line-up for the roadshow, to give you a better sense of what to expect: 9 webinars – These webinars (remote) will be conducted by industry experts and are aimed at increasing analytics awareness, providing a way for aspirants to interact with industry practitioners and getting their tough questions answered. 11 workshops – The workshops will be in-person events to take these interactions to the next level. These would be spread across 6 cities – Delhi, Bengaluru, Hyderabad, Chennai, Mumbai and Pune. So, if you are in any of these cities, we are looking forward to interact with you. Featured Data Science program for you: Master of Science in Data Science from from IIIT-B 2 Conclaves – These conclaves are larger events with a pre-defined agendas and time for networking. The first conclave is happening on the 17th of December in Bengaluru.  Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications Hackathon – Time to pull up your sleeves and showcase your nifty skills. We will be announcing the format of the event shortly. “We find that the IT in­dustry is ab­sorb­ing al­most half of all of the ana­lyt­ics jobs. Banking is the second largest, but trails at al­most one fourth of IT’s re­cruit­ing volume. It is in­ter­est­ing that data rich in­dus­tries like Retail, Energy and Insurance are trail­ing near the bot­tom, lower than even con­struc­tion or me­dia, who handle less data. Perhaps these are ripe for dis­rup­tion through ana­lyt­ics?” Our learners also read: Learn Python Online for Free Mr. S. Anand, CEO of Gramener, wonders aloud. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? upGrad’s Exclusive Data Science Webinar for you – Watch our Webinar on The Future of Consumer Data in an Open Data Economy document.createElement('video'); https://cdn.upgrad.com/blog/sashi-edupuganti.mp4   Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.
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by Apoorva Shankar

15 Dec'16
What’s Cooking in Data Analytics? Team Data at UpGrad Speaks Up!

5.22K+

What’s Cooking in Data Analytics? Team Data at UpGrad Speaks Up!

Team Data Analytics is creating the most immersive learning experience for working professionals at UpGrad. Data Insider recently checked in to me to get my insights on the data analytics industry; including trends to watch out for and must-have skill sets for today’s developers. Here’s how it went: How competitive is the data analytics industry today? What is the demand for these types of professionals? Let’s talk some numbers, a widely-quoted McKinsey report states that the United States will face an acute shortage of around 1.5 million data professionals by 2018. In India, which is emerging as the global analytics hub, the shortage of such professionals could go up to as high as 200,000. In India alone, the number of analytics jobs saw a 120 percent rise from June 2015 to June 2016. So, we clearly have a challenge set out for us. Naturally, because of acute talent shortage, talented professionals are high in demand. Decoding Easy vs. Not-So-Easy Analytics What trends are you following in the data analytics industry today? Why are you interested in them? There are three key trends that we should watch out for: Personalization I think the usage of data to create personalized systems is a key trend being adopted extremely fast, across the board. Most of the internet services are removing the anonymity of online users and moving towards differentiated treatment. For example, words recommendations when you are typing your messages or destinations recommendations when you are using Uber. Our learners also read: Learn Python Online for Free End of Moore’s Law Another interesting trend to watch out for is how companies are getting more and more creative as we reach the end of Moore’s Law. Moore’s Law essentially states that every two years we will be able to fit double the number of transistors that could be fit on a chip, two years ago. Because of this law, we have unleashed the power of storing and processing huge amounts of data, responsible for the entire data revolution. But what will happen next? IoT Another trend to watch out for, for the sheer possibilities it brings. It’s the emergence of smart systems which is made possible by the coming together of cloud, big data, and IoT (internet of things). Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses What skill sets are critical for data engineers today? What do they need to know to stay competitive? A good data scientist sits at a rare overlap of three areas: Domain Knowledge This helps understand and appreciate the nuances of a business problem. For e.g, an e-commerce company would want to recommend complementary products to its buyers. Statistical Knowledge Statistical and mathematical knowledge help to inform data-driven decision making. For instance, one can use market basket analysis to come up with complementary products for a particular buy. Technical Knowledge This helps perform complex analysis at scale; such as creating a recommendation system that shows that a buyer might prefer to also buy a pen while buying a notebook. How Can You Transition to Data Analytics? Outside of their technical expertise, what other skills should those in data analytics and business intelligence be sure to develop? Ultimately, data scientists are problem solvers. And every problem has a specific context, content and story behind it. This is where it becomes extremely important to tie all these factors together – into a common narrative. Essentially all data professionals need to be great storytellers. In this respect, one of the key skills for analysts to sharpen would be, breaking down the complexities of analytics for others working with them. They can appreciate the actual insights derived – and work toward a common business goal. In addition, what is as crucial is getting into a habit of constantly learning. Even if it means waking up every morning and reading what’s relevant and current in your domain. Top Essential Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Certifications Inferential Statistics Certifications 2 Hypothesis Testing Certifications Logistic Regression Certifications 3 Linear Regression Certifications Linear Algebra for Analysis Certifications What should these professionals be doing to stay ahead of trends and innovations in the field? Professionals these days need to continuously upskill themselves and be willing to unlearn and relearn. The world of work and the industrial landscape of technology-heavy fields such as data analytics is changing every year. The only way to stay ahead, or even at par with these trends, is to invest in learning, taking up exciting industry-relevant projects, participating in competitions like Kaggle, etc. How important is mentorship in the data industry? Who can professionals look toward to help further their careers and their skills? Extremely important. Considering how fast this domain has emerged, academia and universities, in general, have not had the chance to keep up equally fast. Hence, the only way to stay industry-relevant with respect to this domain is to have industry-specific learning. This can only be done in two ways – through real-life case studies and mentors who are working/senior professionals and hail from the data analytics industry. In fact, at UpGrad, there is a lot of stress on industry mentorship for aspiring data specialists. This is in addition to a whole host of case studies and industry-relevant projects. Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences?   Where are the best places for data professionals to find mentors? upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 While it’s important for budding or aspiring data professionals to tap into their networks to find the right mentors, it is admittedly tough to do so. There are two main reasons that can be blamed for this. First, due to the nascent stage, the industry is at, it is extremely difficult to find someone with the requisite skill sets to be a mentor. Even if you find someone with considerable experience in the field, not everybody has the time and inclination to be an effective mentor. Hence most people don’t know where to go to be mentored. That’s where platforms like UpGrad come in, which provide you with a rich, industry-relevant learning experience. Nowhere else are you likely to chance upon such a wide range of industry tie-ups or associations for mentorship from very senior and reputed professionals. How Can You Transition to Data Analytics? What resources should those in the data analytics industry be using to ensure they’re educated and up-to-date on developments, trends, and skills? There are many. For starters, here are some good and pretty interesting blogs and resources that would serve aspiring/current data analysts well to keep up with Podcasts like Data Skeptic, Freakonomics, Talking Machines, and much more.   This interview was originally published on Data Insider.  
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by Rohit Sharma

23 Dec'16