Data Scientists are responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face competition in the market.

Data Science is a versatile field that includes Statistics, mathematics, Programming, etc. So let us see what it takes to become a proficient Data Scientist.

1. Statistical and Probability Skills ->

Data Science is basically the rebranding of Statistics. Statistical and Probability knowledge is a very important aspect of becoming a Data Scientist. Before Statisticians with formal degrees could study Data Science but now Data Science can be studied without any formal degree. Generally, Statistics is divided into two categories:

- Descriptive Statistics – Descriptive Statistics deals with summarizing and describing the data. The tools of measurement in descriptive statistics are normal distribution, variability, central tendency, kurtosis, and skewness, etc.
- Inferential Statistics – Inferential Statistics deals with concluding from the data. It draws the conclusion from a smaller sample and implying the drawn conclusion in the large group. Some methods of Inferential Statistics are central limit theorem, hypothesis testing, ANOVA, quantitative data analysis.
- Probability – In addition to Statistical Skills, the Data Scientists also required Probability skills. Data Scientists must be skilled in it to carry out complex machine learning operations. It is used in the inferential and designing of Bayesian Networks. Moreover, Data scientists must be accustomed to conditional probability as it is used in the machine learning algorithms like Naive Bayes.

2. Mathematical Skills ->

For becoming proficient in Data Scientists, then you must be proficient in several topics of Mathematics that include linear algebra, calculus, discrete math, and optimization theory. The different aspects of these topics are

- Linear Algebra

Linear Algebra powers everything that runs on Machine learning. It is used in the artistic rendering of your photographs, recommendation system, and facial recognition. It includes topics like matrices, tensors, matrix factorization, eigenvalues, etc.

- Calculus

Calculus is used in calculating loss function which is the most important concept in optimizing models. The concepts of partial derivates are also used in backpropagation for neural networks. Calculus topic includes maxima & minima, functions of single and multiple variables, partial derivatives, differential equations, etc.

- Discrete Maths

Discrete math is the study of values that are distinct and separate. It is used for dealing with databases. The topics included in discrete math are boolean algebra, set theory, relations and functions, number theory, recursion, graph theory, etc.

- Optimization Theory

Optimization theory helps in finding the most optimal solution in a complex multi-dimensional space. There are 3 types of optimization- variables, constraints, and objective function.

3. Programming Skills

Programming Skills allow you to implement your statistical thinking in a practical setting. Without programming, you cannot put your knowledge to practice.

- Python

Python is highly versatile, it can be used for different tasks and operations. It has a wide range of libraries and functions that can implement in code to develop robust models. It includes libraries they are pandas, Matplotlib, Numpy, Scikit-Learn, TensorFlow, etc. If you haven’t yet started with python, from here you can Learn Python for Free 240+ Tutorial.

- R

R is a statistical programming tool that is used for solving core-data problems. R offers over 10,000 packages in its CRAN repository. It is used in various fields like astronomy, biostatistics, genomics, finance, etc. the important R packages in Data Science includes ggplot2, dplyr, purrr, shiny, etc. From here You can Learn R programming for free.

- Tableau

A tableau is visualization software that allows you to develop and share interactive visualizations. Various types of visualizations in Tableau are bar charts, line charts, pie charts, maps plots, scatter plots, Gantt charts, heatmaps, etc.

- Database Query Language

There are two types of database query languages, they are Structured Query Language (SQL) and non-Relational Structured Query Language (NoSQL). Some of the SQL languages are MySQL, PL/SQL, etc. and NoSQL languages are MongoDB, Cassandra, Redis, etc.

EXTRA SKILLS ->

- Business and Domain Knowledge

As a data scientist you are going to assist your organization for making smarter decisions and to predict outcomes. hence you should have a good business knowledge and predictive analytics skills.

- Excellent Communication Skills

Communication skills is important as data scientist. Read the best books for data science like- Data science for business, Doing Data Science , introducing data science, etc.

- Big Data technologies

The knowledge of Big Data is highly treasured by the industries. To achieve an established position as a Data Scientist, you must require skills of Big Data. big data some trending big data technologies are Apache Hadoop, Apache Spark, Apache Flink, etc.