The emergence of Big Data has given birth to one of the most lucrative careers of the 21st century – the Data Scientist. The term ‘Data Scientist’ has been making headlines for quite some time now.
In fact, Data Scientist is one amongst the top 3 job positions on LinkedIn.
The above fact speaks volume to strengthen the fact that professionals from various backgrounds – Mathematics, Computers, Management, Statistics – are looking to make the most out of this opportunity.
But as with everything that gets thrown around a lot, the term ‘Data Science’, and therefore the job of a Data Scientist, has become largely vague. So, before we talk about the topic at hand, let’s look at what is it that a Data Scientist does.
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What does a Data Scientist do
In simple words, a Data Scientist is an expert professional who deals extensively with Big Data. Data Scientists use a combination of Machine Learning, Artificial Intelligence, Statistics, and analytical tools to extract meaningful information from massive datasets. Unlike before, when datasets were mostly structured, the data at our disposal today is largely unstructured. So, naturally, Data Scientists spend a significant amount of their time in gathering, cleaning, and munging the data to enable its analysis and interpretation.
The job role of a Data Scientist involves an amalgamation of mathematical, statistical, analytical, and programming skills. On any typical working day, a Data Scientist dons on many diverse roles throughout the entire course of the day – from being a Software Engineer and Data Miner to a Data Analyst and Troubleshooter, a Data Scientist also acts as the vital communication link between the IT and the business domains of a data-driven enterprise. It is Data Scientists who help Business Analysts to use the interpreted data in ways that can optimize business benefits.
To be precise, Data Scientists help companies manage and interpret data to solve complex business problems.
If you can picture yourself dealing with Big Data and performing such varied duties in the future, the job of a Data Scientist is your professional calling! However, to become a Data Scientist, you must first acquire the essential skills that are intrinsic to this profession.
Like we mentioned before, Data Science demands specific skills. Thus, to become a Data Scientist, you must bear the following set of skills:
- Flair in programming
To become a Data Scientist, the first rule is to have an impeccable knack for programming. So, you’ll have to have a solid knowledge of both statistical programming languages like Python or R or Java, and database querying languages like SQL, CQL, and so on. Companies, too, look for applicants who have command over at least two or more than two programming languages.
- Knowledge of Multivariable Calculus & Linear Algebra
You may wonder why would a Data Scientist need to master Multivariable Calculus & Linear Algebra. It’s simply because having a solid understanding of Multivariable Calculus & Linear Algebra is immensely beneficial for data-driven organisations where even a minor alteration/improvement in algorithm optimization can deliver groundbreaking business opportunities.
- Familiarity with the basics of Statistics
A big part of the job of a Data Scientist requires dealing in Statistics. Every aspiring Data Scientist must have in-depth knowledge about statistical concepts like Descriptive Statistics (mean, median, range, standard deviation, etc.), Probability Theory, Bayes Theorem, Exploratory Data Analysis, Percentiles and Outliers, Random Variables, Cumulative Distribution Function (CDF), to name a few. The better you understand these concepts, the better you’ll be able to predict the validity of statistical approaches.
- An understanding of Artificial Intelligence (AI) and Machine Learning (ML)
AI and ML ate two integral parts of Data Science, and hence, proficiency in these is a must. Surprisingly enough, not many Data Scientists are well-versed in AI and ML concepts and techniques. So, if you wish to stay ahead of the competitive curve, you better brush up on AI and ML concepts including Supervised ML, Unsupervised ML, Reinforcement Learning, Natural Language Processing (NLP), Recommendation engines, Outlier detection, and Survival analysis, among other things. Also, if you are proficient with ML techniques like decision trees, logistic regression, k means clustering, Naïve Bayes classifier algorithm, etc., you can solve a host of Data Science problems.
- Interests in Data Wrangling
Data Scientists often deal with large, unstructured/semi-structured datasets that only keeps on increasing by the minute. As a result, they have to put a lot of effort into organising and cleaning the messy and complex datasets to enable easy analysis and interpretation. This process is known as Data Wrangling. What Data Scientists do is that they manually convert or map data from one raw format into another more convenient format, so that it becomes easy to keep the data organized and appropriate for interpretation and analysis. Therefore, as an aspiring Data Scientist, you must know how to deal with imperfections and glitches in data.
- Knowledge of Data Visualization
For professionals handling the business side of a company, it is difficult to make sense of raw data. This is where Data Scientists act as a crucial link between the IT and the business wings. After analysing and interpreting the data, Data Scientists visualize the data with the help of data visualization tools like Tableau, Matplottlib, ggplot, and d3.js. Further, they communicate their findings to both technical and non-technical staff for their ease of understanding. With the visual representation of data, it becomes easier for the non-technical members to understand how they can use the data insights to optimize business operations and stay a step ahead of their rival companies.
- Sense of Data Intuition
Apart from being an extremely handy day-to-day tool for Data Scientists, Data Intuition is also a crucial part of job interviews. During interviews, employers will put all your abilities to test, including your intuitive ability to understand concepts related to Data Science. This is what we call ‘Data Intuition.” While it is true that you need to have strong mathematical, statistical, and visualization skills, you also should be able to determine what methods and techniques to use to solve a specific problem, what tools to use, and so on.
Now that you know what skills you need to acquire to become a Data Scientist let’s look at the steps that will get you there!
How to be a Data Scientist – The learning path
The path to becoming a Data Scientist is pretty straightforward. It starts from the start. Let’s walk you through it!
- Beginning it all.
The first step involves understanding what Data Science is all about. Apart from learning all the basic concepts of Data Science, this is the stage where you make a choice of your first programming language and perfect it. The first few months will involve coding in the language of your choice. Once you are adept at coding in a particular language, learning other programming languages will become way more comfortable.
- Learning the basics of Mathematics and Statistics.
Mathematics and Statistics make up the foundation for ML algorithms. Naturally, you’ll have to learn the basic concepts of Maths and Stats such as Mean, Median, Mode, Variance, Conditional Probability, Hypothesis Testing, Linear Algebra, Calculus, Descriptive Statistics, and Inferential Statistics, among other things.
- Learning ML concepts and their applications
After mastering Maths and Stats concepts, it is time to move on to a more advanced area – Machine Learning. ML algorithms have found application in numerous real-world scenarios – from fraud detection and recommendation engines to sentiment analysis of customer feedback. Apart from the concepts mentioned before, you’ll also have to learn about Deep Learning, Artificial Neural Networks, Inductive Learning, etc. Gradually, as you get a hold of these ML concepts, you’ll have to experiment with them in real-world models through various validation strategies.
- Introduction to Deep Learning
A subset of ML, Deep Learning, deals in algorithms that draw inspiration from the structure and function of brain-like artificial neural networks. These artificial neural nets imitate the functioning of the human brain. Deep learning models have at least three layers in which each layer receives information from the previous layer and passes it on to the next one. You must fully understand the functioning of Deep Learning, and to understand it, you’ll have to be well-versed in Linear and Logistics Regression.
- Deep Learning Architectures
After getting the hang of Deep Learning, you must dive in to learn about advanced Deep Learning architectures like AlexNet, GoogleNet, recurrent neural networks (RNN) convolutional neural networks (CNN), region-based CNN (RCNN), SegNet, generative adversarial network (GAN), etc. Since these are quite hefty concepts, you need to dedicate a few weeks solely in understanding their functioning.
- Computer Vision
Computer Vision (CV) is a scientific domain of study that seeks to find ways and develop techniques that will allow computers to understand digital content like videos and photographs. It involves “acquiring, processing, analyzing and understanding digital images” to attain highly specialized data from the real world to create numerical/symbolic information further. Being one of the hottest areas of exploration now, every aspiring Data Scientists needs to have a good knowledge of Computer Vision.
Natural Language Processing is an integral component of Data Science. Thus, every Data Scientist must have a strong understanding of NLP and its techniques. Primarily, NLP seeks to process, analyze, and understand natural language-based data (text, speech, etc.) through a combination of sophisticated tools and algorithms. While dealing with NLP, you’ll be learning about Data Retrieval (along with Web Scraping), Text Wrangling, Named Entity Recognition, Parts of Speech Tagging, Shallow Parsing, Constituency and Dependency Parsing, and Emotion and Sentiment Analysis.
Every day, the global data continues to increase, and with it is expanding the scope for innovation and creation. As Big Data and Data Science technologies continue to advance, the job portfolio of Data Scientists will also change in keeping with the times. So, how then, do you keep up? By upskilling. Data Science is a dynamic field that’s still evolving. To becomes a Data Scientist, you must always harbor an unquenchable thirst for knowledge and learning. If you do so, there’ll be nothing to stop you from shining in the field of Data Science.
Are the terms Deep learning and Machine learning different from each other?
Machine learning is utilized in many apps on our phones, including search engines, spam filters, websites that provide personalized recommendations, banking software that detects odd transactions, and speech recognition. Deep learning is a kind of machine learning in which algorithms are organized in layers to build an 'artificial neural network' that can learn and make decisions on its own. Deep learning is a subset of machine learning in the practical sense. Actually, deep learning is a type of machine learning that works similarly to traditional machine learning. As a result, the names are occasionally used interchangeably. While simple machine learning models do improve over time at whatever task they are given but they still require some supervision. With the use of a deep learning model, an algorithm can use its neural network to assess if a prediction is correct or not.
Is Natural Language Processing (NLP) important in Data Science?
The art and science of collecting information from text and putting it into computations and algorithms is known as Natural Language Processing (NLP). It remains a must-have for all data scientists, given the proliferation of data on the internet and social media. NLP is critical because it aids in the resolution of language ambiguity and provides valuable mathematical structure to data for a variety of downstream applications, such as speech recognition and text analytics. When faced with the task of analyzing and constructing models from textual data, it is necessary to be familiar with basic Data Science tasks.
What should a data science portfolio contain?
Strong data science portfolios generally show an applicant's technical talents, originality in developing research topics, ability to analyze data and make conclusions, desire to work with others, and ability to clearly explain their results to audiences that aren't technical. Your portfolio should, in general, highlight your finest or most recent work. While data analytics portfolios are often used to showcase your work, they should also emphasize your personality, communication abilities, and personal brand.