Complete Roadmap for Becoming a Data Scientist

Published by Rana Jay Pratap Singh on

Data science at its most basic level is defined as using data to obtain insights and information that provide some level of value. In this article you will get idea about how to become an expert in the field of data science. Here is all the important topics and instructions.

Also you can get the study links by commenting below or contacting me on LinkedIn whose link is given below. Let’s deep drive into each step and see what all topics one needs to learn.

1. Excel and Statistics

The first step you must know in the field of data science is Microsoft Excel. Microsoft excel is probably the most well-known and basic tool for data. There is no better editor for two dimensional data. The tables are easily edited, formatted, colorized and shared in Microsoft Excel.

Statistics is a mathematical science pertaining to data collection, analysis, interpretation and presentation. Statistics for data science is used to process complex problems in the real world so that data scientist and analyst can look for meaningful trends and changes in data.

2. Python Programming Language

There are many programming language that we can use like R and Julia but in data science we will use python programming language. Python provide great functionality to deal will mathematic, statistics and scientific functions. It provides great libraries to deals with data science application.

One of the main reasons why python is widely used in the scientific and research communities is because of its ease of use and simple syntax which makes it easy to adapt for people who do not have an engineering background.

3. Numpy and Pandas

Numpy provides an efficient interface to store and operate on dense data buffers. In some ways, numpy arrays are like python’s built in list type but numpy arrays provide much more efficient storage and data operations as the arrays grow larger in size.

Pandas is an open source python library that is built on top of numpy. It allows you do fast analysis as well as data cleaning and preparation. An easy way to think of pandas is by simple looking at it as python’s version of Microsoft’s excel.

4. Visualization Libraries (Matplotlib and Seaborn)

Matplotlib is a plotting library for the python programming language and its numerical mathematics extension numpy. It provides an object-oriented API for embedding plots into applications using general purpose GUI toolkits like Tkinter, wxPyhton, Qt or GTK+.

Seaborn is a python data visualization library based on matplotlib. It provides a high level interface for drawing attractive and informative statistical graphics.

5. Exploratory Data Analysis

Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods. It can also help determine if the statistical techniques you are considering for data analysis are appropriate.

6. Machine Learning

Machine learning is part of data science. It draws aspects from statistics and algorithms to work on the data generated and extracted from multiple resources. Data science is an all-encompassing term of machine learning for functionality.

7. Deep Learning

Data science represents the entire process of finding meaning in data. Machine learning algorithms are often used to assist in this search because they are capable of learning from data. Deep learning is a sub field of machine learning but has improved capabilities.

8. SQL and MongoDB

SQL is needed for data scientist to get the data and to work with that data. Everyone is busy to learn R or Python for data science but without database data science is meaningless.

MongoDB is an unstructured database. It stores data in form of documents. MongoDB is able to handle huge volumes of data very efficiently and is the most widely used NoSQL database as it offers rich query language and flexible and fast access to data.

9. Tableau

Tableau is one of the best data visualization tools used by data science and business intelligence professionals today. It enables you to create insightful and impactful visualizations in an interactive and colorful way. Its use is not just for creating traditional graphs and charts.

10. Power BI

Power BI is a cloud-based business analytics service from Microsoft that enables anyone to visualize and analyze data, with better efficiency and speed. Many businesses even consider it indispensable for data science related work.

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