A Simple Beginner’s Guide for Learning Data Science
By: Kalaiyarasi Murugaperumal
Tags: Data Analysis, Data Science Courses, Machine Learning, Dataanalyticscourse, Data Science

Data science covers very vast chapters, as a beginner, it might be difficult to decide where to start.

Data Science involves various steps from Data collection to Model prediction.

As a beginner, these words can sound like it is complex to learn, but with interest and passion, you can learn all of it.

I Am listing down these steps as I find it to be the easy way to start learning and in the order of priority when it comes to any interviews.

Photo by Charles Deluvio on Unsplash

SQL (Structured Query Language):

SQL plays a major role in learning Data Science. Database is where you collect the data and convert it in to any usable format like CSV for working around the data.

The most asked question in almost every interview and the initial screening rounds will be SQL.

SQL Create, Insert, Select, grouping, SQL joins, Sub query and analytic functions are the areas you need to be very strong with the basics.

Hacker rank is one of the sites where you can practice SQL statements and there are many resources available online where you can learn the basics.

You can install any IDE like SQL workbench to practice these SQL queries.

Python Programming:

Next you can start with Python basics. If you are from a non-programming background start with basic syntax of Python, Variables, datatypes, string operations, logical operators, conditional operators, loop statements, List, tuple, dictionary.

Coming to working with Data there are many inbuilt libraries like Pandas, Numpy which will be the basic libraries you need to know, this will be the most used libraries when you start working with data for analysis purpose.

You can install Anaconda environment where you will get Jupyter notebook an IDE to start coding.

Statistics:

Statistics is the core component you have to know before you jump in to Machine Learning concepts.

Statistics fundamentals are some of the concepts we already learnt in middle school. Probability, Sampling, Mean , Median, Mode, Distribution, confidence interval, Hypothesis, Chi square , ANOVA.

Data Visualization:

Data visualization can be performed by using Python libraries like Matplotlib, Seaborn these libraries you can learn once you know the basics of Python and the Python libraries I mentioned above.

The other tools you can learn is either Tableau or Power BI to present the data in the form of attractive dashboards.

Machine Learning:

Here comes the most critical part. Before you start learning any algorithm understand what is the use of that algorithm, what are its pros and cons and what is the type of data can be used for that algorithm.

Machine learning algorithm you need to know(In the order of learning):

Supervised Learning- Linear Regression, Classification- Logistic Regression, Decision tree, Random forest, Support vector machine(SVM). Unsupervised Learning- K-means, KNN, Apriori, ARIMA. And Ensemble methods like Bagging and Boosting.

I have listed down everything in the beginner’s perspective,I hope it helps you to start your learning.

Apart from learning you need to start practicing them using datasets.These data sets will be available on Kaggle for download and also you can participate in Kaggle competitions to get an insight of how everyone approach the same business problem in various ways. You can also refer to Analytics vidhya for any hackathons.