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Richa Bhatia

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Richa Bhatia is a seasoned journalist with six-years experience in reportage and news coverage and has had stints at Times of India and The Indian Express. She is an avid reader, mum to a feisty two-year-old and loves writing about the next-gen technology that is shaping our world.

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Want to Be a Data Analyst? Here are Top Skills & Tools to Master
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Want to Be a Data Analyst? Here are Top Skills & Tools to Master

Crunching numbers and spotting patterns has become the gold standard in the IT industry. Data analyst jobs are in demand, LinkedIn’s Most Promising Jobs of 2017 has listed Data Engineer at number 9 and Analytics Manager making it number 18. Another Glassdoor study of the 50 Best Jobs in America puts Data Scientist at the top spot with Data Engineer coming in at a close number 3 and Analytics Manager at an enviable number 5. However, taking the top slot isn’t easy. You need an armory of data analytics skills if you want to clock year-on-year growth and a fat pay package with this major career advancement. While the internet is abuzz with free resources on how to master the fundamentals of data science, sentiment analysis, and fast track machine learning among others. Analytics India Magazine and UpGrad help you cut through the claptrap by listing down 5 basic skills needed to become a data analyst.   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 Put yourself in the lead for Data Analyst jobs with these skills Let’s start with a few basics: Educational Background Not everybody can become a data analyst. You need to have a natural leaning toward math and statistics. All those years of learning calculus and probability will come in handy. A degree in Computer Science is always an added advantage. Statistics: To become a full-fledged data analyst, a thorough grounding in statistics is essential. Being good at statistics will help you understand algorithms deeply and also when they should be used. Brush up on applied statistics, linear algebra, real analysis, graph theory and numerical analysis. Linear algebra comes into play with regression, understanding data structures and preparing data for prescriptive and predictive data modeling. 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 Our learners also read: Free Online Python Course for Beginners Programming Skills 1) Statistical Language: SAS vs R vs Python: It’s a question that needles most data nerds when it comes to picking up the analytical tool of choice. While SAS (expensive) and Python (billed for low-scale data processing) are easy to learn, R (low-level programming language) wins hands down thanks to its advanced computing capability, better graphical capabilities and advanced tools. Since R is open sourced, features and packages get added quickly as opposed to SAS. Another reason why R is thriving is it has a huge ecosystem backing it up that keep it up-to-speed with rich features. Pro Tip: R’s commercial appeal has made it a household (read: IT/tech focused companies’) name and while SAS is still widely used by enterprises, this statistical language is catching on. But R has a steep learning curve. 2) Querying Language: SQL: One of the oldest querying languages, SQL is a general-purpose database language which is used for analytical as well as transactional queries. SQL is mainly used in day-to-day operations and cannot support petabytes of data. Programs like Unity tutorial can help you familiarize yourself with PHP & MySQL more in-depth. Hive: This Hadoop query language was invented by Facebook’s Data Infrastructure team. Right from the day that Hive was open sourced in 2008, it has become the popular choice for business analysts. The open source data warehousing solution that uses an SQL type language called HQL can support terabytes and petabytes of data as opposed to SQL. The downside is it only supports structured data. PIG: One of the biggest advantages for Pig is that it can process both structured and unstructured data and works over MapReduce. It is the go-to language for most programmers who tend to write scripts. What you need to do is learn Pig Latin that helps tackle structured/unstructured and semi-structured with more ease as compared to Hive. Here’s a bit of history trivia – Pig was created in Yahoo in 2006 to perform MapReduce jobs. Pro Tip: Knowledge of SQL will help in picking up Pig and Hive. 3) Scripting Language: MATLAB: It’s a language used for data mining. Some might argue that its popularity has declined. It wouldn’t hurt to put it in your arsenal. Remember, MATLAB has been around for a long, long time, invented in the late ’70s as a tool for data analysis. Python: This is hands down one of the most popular scripting languages and its popularity stems from current stack. The core libraries NumPy, SciPy, Pandas, matplotlib, IPython. Perfect for modeling and analysis. It has one drawback though – scalability for large datasets. Pro Tip: Python has a strong community and is best used for scraping websites and data engineering. Guess what? It’s so easy that people with a non-programming background can also master it! Machine Learning Machine Learning (ML) is not just a buzz word. It is finding a lot of utility across domains and gaining immense traction, and therefore turning out to be an essential skill that data professionals need to have. In ML, regression, classification and segmentation are the broad learning areas that analysts should focus on. Data Visualization You have all this data; now how do you bring it to life? Your job, as a data analyst, would be to make evocative reports, find trends and communicate these findings to the top brass. Data visualization tools to master are Tableau, Microsoft Power BI, Oracle Visual Analyser, SAS Visual Analytics. If you like R, you can use the ggplot package to create highly interactive charts and graphs. Pro Tip: Don’t just learn the tools. Try understanding the motive of visually encoding data as well. Understanding Databases Essentially used to better understand the customer, database analysis extends from basic analysis to complex data mining through various tools – Geographic Information System (GIS) or text analysis. The basic steps for analyzing databases are to extract, clean, merge, analyse and implement. Checkout: Data Analyst Salary in India 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 – How upGrad helps for your Data Science Career? document.createElement('video'); https://cdn.upgrad.com/blog/alumni-talk-on-ds.mp4   Data Munging or Data Wrangling Before you start extracting insights from reams of data, data must be cleaned. In plain speak, somebody needs to do the job of a janitor, which means, manually cleaning data and processing it in a unified format before it is analyzed. So far, excel has been used for cleaning and enriching data, but Stanford debuted an interactive tool, a work-in-progress called Wrangler. Pro Tip: Give Wrangler a try and see how you can manipulate real-world data and export it for use in Tableau or R A data analyst does not requires advanced skills like data scientists. However, since these roles are multi-faceted and learning is a continuous process, with additional resources you can become a junior data scientist as well. Essentially, mathematics and statistics (32%), computer science (19%), and engineering (16%) are predominantly the most important fields of study for a data scientist. Data analysts are generally expected to be proficient with languages such as SAS and/or R. It’s advisable for people with a computer science background to know Python, Hadoop, and SQL coding. Additionally, working with unstructured data is an integral part of the data analyst job. It’s a good idea to be accustomed to unstructured databases. Moreover, a data analyst must imbibe qualities such as developing a business acumen or good communication/presentation skills, as these skills will help stay ahead of the game. 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. This post was originally published in Analytics India Magazine.  

by Richa Bhatia

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15 Feb 2017

Data Analytics Is Disrupting These 4 Martech Roles – Which Role Suits You Best?
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Data Analytics Is Disrupting These 4 Martech Roles – Which Role Suits You Best?

Predictive analytics in marketing technology, (martech) is passé!  And while over the years data analytics has become the baseline skill for marketers across industry verticals, analytics is still driving big change. This change is not just limited to staffing in a marketing department, but also in attracting data-savvy professionals who can combine financial, marketing and analytical skills. According to a recent Gartner CMO spend report, CMOs now allocate 27% of their budget towards technology. The increased spend highlights the use of leading-edge technology in providing top-notch customer experience and fine-tuning marketing performance, not to forget to outclass the competition. Data analytics has become the standard practice across domains and we believe it is time to look at new analytics functions that will drive forward-thinking organizations that want to make it big in digital. As the McKinley Marketing Partners report sums up, digital marketing is on the rise, but the marketing professionals of the future must be both experienced marketers and critical thinkers, with the ability to analyze data. With marketing on the path to automation, Analytics India Magazine and UpGrad narrow down a bunch of emergent data analytics job functions in martech. The martech landscape revolves around these clusters: Advertising Content Social & Behavioral Commerce & Sales Data Management The future of marketing is shaped by new roles in data analytics. Here are the skills that are most in-demand: 1) Algorithmic-led marketing: With search becoming predictive, what with Facebook, Twitter and Google giving tailored recommendations, brands need more behavioral data powered by Artificial Intelligence (AI) to better understand the customer decision journey, drive engagement and cross-sell products. Another area where AI and machine learning comes into play is in dynamic pricing. Case in point – cab aggregating apps Ola and Uber’s surge-pricing algorithm (wherein prices peak when the demand is high). Through dynamic pricing, brands can see substantial gains in profit margins. Check out the Master’s Degree program in Data Science from LJMU  upGrad’s Exclusive Data Science Webinar for you – Watch our Webinar on The Future of Consumer Data in an Open Data Economy     Data savvy marketing mavens must already be familiar with Programmatic advertising, hailed as the future of advertising wherein cutting-edge machine learning algorithms are used to automate targeted ads for customers. Case in point is Programmatic advertising for media buying that enables the brand to customize a message for the right audience via audience insights from the brand for search, display and social media.  Over the years, programmatic advertising has moved into the mainstream with data-driven advertising companies using it for informing TV and radio advertisement spends as well. The advantages are manifold – optimize KPIs in real time and utilizing data from multiple platforms as users move from screen to screen. Who’s fit for the job role: Data Scientist, Data Analyst & Machine Learning Engineer Skills Required: Researching and understanding user behavior patterns such as customer engagement, building machine learning model prototypes in R/Python for in-depth analysis. Broadly speaking, one needs to have a solid foundation in CS and statistics, math, modelling and analytics. Proficiency in: probability and statistics ML algorithms and libraries computer science fundamentals & programming data modeling & evaluation are necessary Average Pay Package: 12- 18 lakhs per annum Relevant Companies: Amazon India, Google, Flipkart, Adobe, InMobi, Mu Sigma, start-ups such as Crayon Data and Bridgei2i 5 New Data Analytics Roles that will Define the Future of Banking 2) Mining the Social Buzz: Customer listening or social listening as it is popularly known is the #1 priority in organizations across the board. From monitoring brand engagement across digital touch-points to customer engagement, the job entails driving personalized real-time actions across channels. The demand for social media analytics is emphasized by a research report by McKinley Marketing Partners that cites that digital marketing expertise is the most desired skill of 2016. According to McKinley’s research, around 90 percent of marketing roles need analytics or digital marketing experience. Industries, agencies and leading brands have deployed social listening tools to better track and monitor brand health. Who’s fit for the job role: Social Media Listening and Brand Analyst, Social Listening and Digital Insights Manager Skills Required: Job entails analyzing data, based on customer/brand sentiment, product experience, customer relations management and preparing comprehensive reports. The job also involves mining data from social channels and turning into reports that inform inventory decisions. Familiarity with text mining techniques and natural language processing is needed. Industry experience across domains such as BFSI, FMCG and telecom and knowledge of data visualization tools is a must. Proficiency in: making reports using data analytics tool preparing reports using web analytics working with high volume and multi-platform data branding and marketing experience is necessary Average Pay Package:  INR 570,000 onwards Relevant Companies: Nestle, GroupM Unleashing the Power of Data Analytics 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 3) Managing Data Management Platform: DMP is akin to a data warehouse, a platform for storing and analyzing campaign and audience data. It provides one point access to marketers to source and manage data such as cookies and identifiers for data segmentation. The information is used for creating focused campaigns based on segmentation. Some of the big players in the market are Adobe AudienceManager and Oracle DMP. Who’s fit for the job role: DMP Consultant Skills Required: It’s a job that spans all industry verticals, hence a deep vertical industry experience is a plus. Knowledge of digital marketing and Google analytics suite is a plus. This is a customer-facing role that requires identifying new business opportunities. Knowledge of the internet and online advertising, including using data for targeting and measurement is a must have. Proficiency in: making reports using data analytics tool conversant in DMP technology HTML, Java, SQL strong sales acumen online technologies are necessary Average Pay Package: INR 1,24,000 per month Relevant Companies: Adobe, Oracle, Accenture 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 4) Market Research Analysis: To succeed in the age of data-led economy, organizations need to understand what their consumers want. This is where market research analysis comes into play. Analysts study market conditions, consumer behavior and monitor competitor activity by analyzing a vast amount of data through statistics, predictive analytics and data-driven tools. The findings of market analysts have a significant impact on an organization’s products or services. Data-driven marketing plays a huge role in skills thoroughly grounded in data analytics, CRM, customer experience modeling and a solid proficiency in data management systems. Who’s fit for the job role: Marketing Analyst, Market Research Analyst Skills Required: It’s a job that requires working in collaboration with data scientists, statisticians and converting research findings into graphs via data visualization tools. Proficiency in: 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? big intelligence tools such as Tableau information science and statistics visual analytics knowledge of the web and direct marketing are necessary Average Pay Package: INR 826422 onwards Relevant Companies: Deloitte, Accenture, E&Y, HP 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. How Can You Transition to Data Analytics?

by Richa Bhatia

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27 Jan 2017

5 New Data Analytics Roles that will Define the Future of Banking
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5 New Data Analytics Roles that will Define the Future of Banking

This article was originally published in Analytics India Magazine. From personalization to customer-centric approaches, predictive risk management to defining product optimization across various channels. The banking sector has given rise to a new breed of talent across the data analytics career space. According to a recent Mckinsey guide to surviving banking in 2017, Data is the center of every meaningful decision. It leads to an uptick of data scientists and data translators; who analyze large volumes of data and convert them into a new product or drive product enhancement for the end-user. Further, the banking industry’s dependence on quantitative analysts, popularly known as ‘quants,’ has deepened. With the roles becoming more widely spread across banking institutions, this new data talent will define the future of banking. It can combine skills and expertise across major aspects of – big data, analytics, digital, risk mitigation and fraud prevention. Will data science have a better future? Definitely. And there will be a spurt of diverse roles across the financial sector.  As banks grapple with large volumes of data of all sorts – social, text, video and geospatial. Data analysts will play a leading role in the lending sense to data and deriving customer insights such as behavior predictions and delving into customer sentiment. Learn Data Science Courses online at upGrad The key areas where data analytics is applied are customer-centricity, cost containment, combating the cyber threat, global terror, and compliance and risk management. These are the areas where most of the new roles will emerge. Decoding Easy Vs. Not-So-Easy Analytics Analytics India Magazine and UpGrad chalk out a few data analytics roles that will emerge and what goes into their making: 1. Fraud Prevention Analyst Must-Have Skills: A good grounding in statistics, applied mathematics and algorithms. Expertise in Python, Java and knowledge of HBase is a must have. Potential Employers: EY, Genpact, Deloitte, HPE Infosys, TCS Job Role: Predictive analytics was the gold standard in fraud mitigation strategy but what’s come into focus recently is dynamic machine-learning (ML) based system. The scope of Data Analytics is only getting higher. Many financial institutions have deployed it. A case in point is a storied company, Mastercard. At Mastercard, the use of sophisticated algorithms for intelligent analysis not only provides more accuracy but also real-time information that brings down false declines, and increases approvals, for genuine transactions. Also read: Learn python online free! Mastercard’s Decision Intelligence solution, rolled out last year, probes an existing account overtime and detects anomalies such as a spike in spending by leveraging customer’s account information, device, location, type of purchase, merchant etc. The role of an Analyst in banking industry includes providing consultation to customers, planning and executing day to day projects, investing their time in searching if the customer is credit worthy or not and preparing financial and market analysis. Through unique algorithms, banks deploy ML techniques to rule out anomalies in a customer’s spending pattern and leverage artificial intelligence techniques to improve the overall customer experience. How Uber Uses Data Analytics For Supply Positioning and Segmentation? 2. Credit Risk Analyst Must-Have Skills: Besides Statistics, knowledge of Business Intelligence (BI) tools and Risk Modelling Framework are also important. Potential Employers: Accenture, Genpact, SAS, Mckinsey, TCS Job Role: Credit risk management deploys preventive measures and relies heavily on preventive analytics to enable banks to mitigate the likelihood of defaults. Post the 2008 mortgage crisis, banks have strengthened their credit risk portfolio in light of new and tighter regulations that have come into force. The job of a credit risk analyst entails making sound business decisions through advanced credit analytics. This is where prescriptive and predictive analytics comes into play. CRISIL report already pointed out a large talent gap in India, in this area. 3. Data Science Translator in Banking Must-Have Skills: Besides the usual Data Science skill-set, excellent communication; an understanding of industry domain, trends and new technologies is a must-have. Potential Employers: Mckinsey Job Role: It’s a profile that is almost unheard of, so far, and one of the best financial institutions globally, Mckinsey started it. Mckinsey is defined as one of the leading analytics-driven organizations across the globe. They recently posted a new job title for their analytics team. According to Mckinsey, a Data Science translator is one who has a solid base of advanced analytics tools such as Tableau, Hive, Hadoop, Spotfire and a grounding of programming languages like R, Python and SAS. Besides a demonstrated ease of working with huge data sets, a Data Science Translator is proficient in network analytics, customer life-cycle management and has the core skill of converting data into BI and meaningful insights. Data translators would also be having a client-facing role, probably acting as a bridge between the team and clients. How Can I Double My Salary? Data Analytics Is Your Answer upGrad’s Exclusive Data Science Webinar for you – Watch our Webinar on How to Build Digital & Data Mindset? document.createElement('video'); https://cdn.upgrad.com/blog/webinar-on-building-digital-and-data-mindset.mp4   4. Anti-Money Laundering (AML) Must-Have Skills: Knowledge of advanced statistical methods, proven experience in financial data analysis and a familiarity with AML issues. Potential Employers: Infosys, Genpact Job Role: Data Analytics can play a huge role in combating global terror by stemming the flow of funds to terrorist and criminal organizations. AML has grown into a service stream with financial institutions deploying AML-KYC solutions and tapping varying sources of data for leads. The first step in tackling money laundering is to collect the right information for meeting regulatory obligations. This information should be made digestible and visible through the right set of BI tools. To sharpen the process further, another layer should be added to the database to extract the right kind of data which will help in finding the right patterns of data. Genpact, is a global leader in digitally-powered business process management and transformation. They offer advanced analytics tools in AML/KYC that also provide due diligence and a world-class screening platform. 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 5. Customer Service Analytics Must-Have Skills: Proven knowledge of SAS, R, and experience in building marketing analytical models, problem-solving skills and knowledge of techniques such as regression and market basket analysis. Potential Employers: IBM, Salesforce.com, Oracle, TCS Job Role: Want to create customer-centric products by leveraging big data? Want great insights on what might click with the new-age customers before making a sizable investment? You will have to tap into reams of data before uncovering valuable insights such as customer’s pain points and spending behavior. From sentiment analysis to customer-focused products, banks are increasingly driving engaging experiences by building excellent analytical capabilities through Customer Journey Analytics solutions. The analytics platform identifies key opportunities and can also predict future behavior. Top Data Analytics Trends to Follow for a secure future! 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 use of data analytics in customer service encompasses four basic points across which customer journey specialists work: Collect data covering the customer journey, including the varying touch-points. Apply analytics to understand the customer’s pain points and personalise the journey. Use predictive analytics and ML technique to predict future behavior. Rework the platform with the new data discovered. 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? Wrapping up! Future of Data Analytics Well, as we discussed above the scope of Data analytics, which is huge in the near future. The role of Data Analyst in banking is to gather analytical reports from the insights and help the institution and its employees make a better decision for the future. Moreover, the role of Analytics in the banking industry is crucial as the growth of the company is based on the strategies you make to get the desired results.  Another field of analytics includes Data Science translator as discussed above. The Data Science scope in India is valuably increasing as Banking sectors earlier used to avoid Big Data and Data science to avoid fraudulent. Now that the industry is emerging immensely, the scope of Data Analytics in India is increasing in direct proportions. The future of Data analytics depends on Data Science as it deals with both the unstructured and structured data. Then comes cleansing, mining and analyzing data to an extent where you can come to the beneficiary measures which should be initiated for the growth of the company. All of this comes under Data Science. If you are keen on leading data-led innovations in the banking sector which is primed for exponential growth in 2017, there are a plethora of roles to choose. And if you do not have the prerequisite skills or want to up-skill with specialized courses, then you can enroll in UpGrad’s PG Certification in Data science! The course is provided in partnership with IIIT-B. As part of the program, you get the opportunity to specialise in BFSI domain through coursework and through an industry-relevant Capstone project. Check it out now! Also, Check out the Master’s Degree in Data Science from LJMU 

by Richa Bhatia

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19 Jan 2017

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