Five steps to becoming a Data Scientist

Data Science is evolving into one of the fastest-growing and most in-demand fields in the world. It has been stated as,” The Sexiest Job of the 21st Century” by the Harvard Business Review. Data Scientist is not at all a complicated job, anyone whose interest is inclined towards technology, mathematics, statistics or Analytics could take it as a profession.

If you want to become a Data Scientist, what you have to do it at first is to understand the core concepts like, what Data Science is and why is it important? What are its applications?

 

 Five steps to becoming a Data Scientist

What is Data Science?

Data Science is the process of cleaning, massaging and organizing the data to turn into a valuable resource which helps to build business strategies.

Applications of Data Science

Data is increasing day by day which is directly leading to the application of data-driven strategies, helping organizations to make a better decision.

Below are the applications of Data Science,

  1. Recommendation Systems
  2. Fraud transaction identification
  3. Identify sales lead
  4. Cross-selling and up-selling

Once you are clear about the concepts, follow these Five steps to becoming a Data Scientist:

 Five steps to becoming a Data Scientist

Step 1:

Get adapted to,

1. Mathematics

Mathematics is a topic of which many people are scared of, but if you want to be a Data Scientist you should have to get your concepts cleared on data distribution, deviation & variance, Probability theory, Linear algebra and statistics.

Probability: Probability is a measure of how likely something is to happen. In Data Science, there are many events which can not be predicted with total certainty. So the concepts like Probability distribution and Bayes Theorem are much needed to know the Data Science.

Linear Algebra: Linear algebra is the branch of mathematics that deals with vector spaces. It is very important to understand the ideas behind the various techniques of linear algebra such as Eigen value, Regression-matrix multiplication, Clustering, Time series etc. to understand where and how to use it.

Statistics: Statistics is a part of mathematics which is concerned with analyzing and interpreting the data, which will help organizations to make better decisions for the business from data. You should have idea about Data distribution, Chi square analysis, Deviation & variance to know statistics for Data Science.

2. Machine Learning

Machine Learning is a field which gives computers the ability to take a decision based on the previous experience or earlier data. These are the algorithms that help in improving the results. Machine learning can be applied in several fields like,

  1. Banking – to detect fraud transaction
  2. E-commerce – Recommender Systems
  3. Retail – Loyalty Program, Target customers for sales
  4. Health-care- Detection of cancer cells.

Step 2:

Learn Coding

To know the coding required for being a Data Scientist you are supposed to have the knowledge of programming languages like Java, Matlab, SQL, Python.

Java: Java is a general-purpose computer-programming language that is concurrent, class-based, compiled language. This makes it suitable for writing efficient codes and computationally intensive machine learning algorithms.

Matlab: Matlab is Designed for multi-paradigm numerical computing environment. MATLAB is well-suited for quantitative applications with mathematical requirements such as signal processing, Fourier transforms, matrix algebra and image processing.

SQL: SQL (‘Structured Query Language’) is used in managing data, it is well organized at updating, querying and manipulating the data. It helps to fetch data from the tables, modify data, delete data and also to update tables.

Python: Python is a statistical/Programming tool that is used in developing models using Machine Learning Algorithms.

Step 3:

Master Database

To become a Data Scientists you need to understand database because you will be working on lots of data and you will be needing the database to process and manipulate the data.

Step 4:

Expert Visualization and Reporting

Visualization

Visualization is an essential part of Data Science and it is important to enable advanced analysis of data. It involves the study and creation of visual representation of the data, Tableau is a software which is used to present the analysis in a visual way, which is more appealing then presenting the results as numbers.

Reporting

Reporting is providing the analysis and results of data into a report.

Step 5:

Learn Big Data

Big Data refers to a large volume of structured and unstructured data that cannot be processed properly with the traditional applications. Big Data can be used to analyze insights which can help organizations to take a better strategic decision.

Take-Away

The above steps are the building blocks to become a Data Scientist. Data science is not for everyone, but for the interested and the dedicated, it can be incredibly rewarding. As the demand for Data Scientists is increasing day by day, it is the best time for you to grab this exciting. opportunity.