How to Become Data Scientist in 2023

My Friends, I am also doing a course in Data Science At OdianSchool.  Let me tell you why I choose this career. The data scientist is the hot new job of the decade. In fact, the demand for data scientists is so intense that Harvard Business Review referred to it as the sexiest job of the 21st century. According to Forbes, the salary of a data scientist is set to go up by 40 percent to $240,000 in 2022. The race for data science is on and it’s just getting started.

What is Data Science?

Data science is the application of scientific techniques and processes to create, extract, transform or model data for the purpose of understanding and interpreting it to make effective decisions. Data science is a field of study that uses statistics, machine learning techniques, and programming to help businesses make better decisions.

Data Science involves:

  • Statistics
  • computer science (Programming)
  • mathematics & statistics
  • Data cleaning and formatting
  • Data visualization 

How to Learn Data Science?

Normally, to become a data scientist you don’t need Computer Science Degree. data scientists come from different backgrounds and work experience foundations, but you have to master these five things to become a successful data scientist.

  • Domain Knowledge
  • Math Skills
  • statistics skills
  • Computer Science
  • Communication Skill

Domain Knowledge

data scientist work in different fields so domain knowledge is very important in this fiend. for example, if you want to be a data scientist in the marketing field and you have good knowledge of google ads, Facebook ads marketing techniques, etc. so this is going to be very beneficial for you and the marketing firm itself will give more preference to these types of applicants more than a normal applicant. 

Math Skills

Mathematics skills like Linear Algebra, Multivariable Calculus these two things are very important as they help you learn various machine-learning algorithms that play an important role in Data Science.

  •  linear Algebra Topics:
  1. Vectors
  2. Matrices
  3. Transpose of a matrix
  4. The inverse of a matrix 
  5. Determinant of a matrix
  6. Trace of a matrix
  7. Dot product
  8. Eigenvalues
  • Multivariate calculus Topics:
  1. Derivatives
  2. divergence 
  3. curvature 
  4. quadratic approximations

Statistic Skills

Statistics are mostly used in data analysis. understanding Statistics is very significant. Probability is also significant to statistics and it is considered a prerequisite for mastering machine learning.

  1. Understand the Type of Analytics
  2. Probability
  3. Central Tendency
  4. Variability
  5. Relationship Between Variables
  6. Probability Distribution
  7. Hypothesis Testing and Statistical Significance
  8. Regression

Computer Science

there is a variety of topics that you have to learn but when it comes to programming you can learn Python(recommended), R, Java, etc. other than this you have to cover databases, SQL, Git & Github, MongoDB, etc.

  • Python: 
  1. Python Basics
  2. List
  3. Set
  4. Tuples
  5. Dictionary
  6. Function, etc.
  7. NumPy
  8. Pandas
  9. Matplotlib/Seaborn, etc.
  • DataBase:
  1. SQL
  2. MongoDB
  • Other:
  1. Data Structure -Time Complexity
  2. Linux
  • Data Visualization Tools:
  1. Power BI
  2. Tableau

Leave a Comment

Your email address will not be published. Required fields are marked *

Scroll to Top