Data Science Roadmap – An Easy Guide For 2021

Introduction 

As the blog goes on, you will be able to understand what the term data science means and the data science roadmap to help you dig further into this prospering career. Ever casually heard of this word “Data Science” and wondered what it means and why is a career in data science taking a peak nowadays? The term can be quite mysterious and may lead one to wonder what a data scientist actually does! As the world is heading towards a digital revolution, data storage is increasing continuously. Once the problem of data storage is solved, another aspect comes into existence as to how to process the stored data. The secret to this is Data Science.

Data Science requires a mixture of multidisciplinary skills ranging from statistics, mathematics, computer science, business and communication. Data Science is that field of study which combines programming skills, domain expertise, knowledge of mathematics, and statistics, to extract meaningful insights from data. The practitioners of data science use artificial intelligence to perform tasks requiring ordinary human diligence. 

1. Roadmap for data science
2. Data collection & wrangling
3. Machine learning
4. Big data

1) Roadmap for data science 

The roadmap for data science is laid down below. Let’s dive into each step and understand each topic carefully.

• Mathematics Fundamentals

This backbone of data science roadmap includes linear algebra, differential calculus, permutations & combinations, and optimisation. The skills required in Mathematics Fundamentals are machine learning, statistical modelling, experiment design, Bayesian interference, supervised learning – logistic regression, decision trees, random forests, unsupervised learning – dimensionality reduction and clustering, and lastly optimisation – gradient descents and variants. Different topics serve different purposes like gradient descent uses differential calculus and optimisation of the cost function, sigmoid function backs logistic regression, permutation and combinations make the concepts of probability clear which is essential for Naïve Bayes Model and Bayes Theorem.

• Programming

Programming statistics involve data and control structures. Choosing one programming language out of numerous available languages is quite a difficult task. Majority data enthusiasts recommend Python as the prime language, which is easy to learn and widely accepted. The programming language R is also taken into consideration. Once you have decided on the choice of programming language next step in the data science roadmap would be to learn about programming fundamentals. The topics to be covered here are module creation, exceptional handling, control structure, data types, data structured and language fundamental syntax.

• Statistics

This is the next step in the roadmap for data science. There are five types of statistics, namely descriptive statistics, statistical inference, difference statistics, associative statistics and lastly predictive statistical analysis. The different types cover the following:

1. Descriptive Statistics: This covers data summarization, measures of central tendency – mean, median & mode, measures of dispersion – range, standard deviation, variable, inter-quartile range, and measure of shape – skewness & Kurtosis.
2. Statistical Inference: This covers parameter estimation and hypothesis testing.
3. Differential statistics: covers 2 sample hypothesis testing.
4. Associative Statistics: covers correlation analysis – Spearman, Pearson and Kendall.

2) Data collection & wrangling 

Data wrangling is the process of mapping and transforming data from one raw data format to another to make it more valuable and appropriate for a variety of downstream purposes like analytics. It involves data collection, cleaning, visualisation and manipulation. 

• Data visualisation 

To get into data science on must know about the different visualisation techniques available and more importantly, which visualisation technique to use. The visualisation makes a story out of stored data. Many organisations have made billions just by having impressive knowledge about data visualisation. Among BI tools Tableau skills are the most used ones, which are followed by Qlikview & Microsoft Power BI.

3) Machine learning 

Machine learning programmes computers to do tasks without the use of human effort. It emphasises on artificial intelligence making a machine to perform a task without explicitly programmed to do so. The first step in machine learning requires you to understand all its terminologies. Machine learning is of three types – supervised, unsupervised, and semi-supervised.

• Deep learning 

Deep learning is a part of machine learning based on neural networks. Neural networks are machine learning algorithms flourishing in today’s world. They help to achieve exceptional and impressive performance where the traditional model is not good. Topics to be covered include hyperparameter tuning, deep neural networks, activation functions.

4) Big data 

Big data refers to a huge assortment of data which is growing exponentially over time. Big data helps to store a large amount of data, the one which cannot be processed or stored with the help of traditional management tools due to its large size and complexity.

Conclusion 

Follow all the above-given steps and concepts, and you are ready to ace a data science interview. Data Science is becoming more and more popular and interesting in the coming days. This roadmap to data science will surely help you take a step further into this digitalised world. After reading the above blog, we hope you understand the data science career roadmap and hope that it helps you in your decision.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 

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