Data Scientist is the Sexiest Job of the 21st Century and it’s a fact, But what is data science and what do we do in it? What approach do we need to follow?
In today’s market questions like the above are capitalized and different people sell different paths. I’m a Data Engineer and have tried and tested various paths from different gurus. In this article, I’ll take you through the optimized path that I’ve curated for myself and helped me get the job.
One thing is clear we need to have a strong grip on Python. If you do not hold any experience in Python that's fine. Python is an easy language to learn we can start by learning the different data types. The most used data types in Python for data science are pandas and NumPy, Then comes the logic building part. it‘s gradual and consistency will make you perfect.
After Python, you should have a very strong knowledge of Microsoft Excel as it is the most used tool in different companies for the post of data analyst proficiency in excel is a must.
For a beginner data analyst role, one should know of any of the following visualization software: tableau power bi, Cognos Analytics, or Google data studio. The software can bring life to your data and be able to visualize what your data speak.
After learning the visualization tools the next most important thing to learn is the database a very strong grip on SQL is a must as most of the roles deal with huge data that are stored in databases. Almost in every data role, There are several questions asked on SQL. One should have both practical and theoretical knowledge to crack the interviews, which will come through rigorous practice and consistency.
The above 4 points are enough to get you into a business analyst role. But for getting into a data role the grinding is yet to start. The data scientist's knowledge of maths is very important because all the machine learning algorithms function on different maths equations. The topics one should cover are Calculus, vector algebra, and matrix algebra, Probability theory, random variables, discrete distributions, continuous distributions, joint distributions, sampling and statistical inference, confidence interval & hypothesis testing.
Now next comes the machine learning part there are three different types of machine learning algorithms namely regression, classification, and clustering. Right now we will not get into details of what these algorithms do. I will be covering the most important machine learning algorithms that one should have a good knowledge of. Starting with regression and classification the major algorithms are linear regression, regularized linear regression, Logistic regression, polynomial regression, decision trees, Ensembles of decision trees, K nearest neighbors, naive Bayes classifier, support vector machines, and neural networks. Then talking about clustering algorithms, we have KMeans, Hierarchical Clustering, and DBSCAN.
Now for the final touch, one should practice case studies, business use cases, and guesstimates of different machine learning algorithms in different businesses. In most of the interviews, the interviewer will check your application knowledge about the machine learning algorithms that you have learned And your ability to think and structure problems. I feel a lot of problems in this section in the beginning but with practice over the months, I was finally able to grasp it.
With this 7-step roadmap, you will be ready to face a data science interview and scale your skills by getting into deployment and deep learning.
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