Skills required for a Data Scientist, Data Science is one of the fastest-growing fields including job opportunities in 2021. According to major recruiters, the average pay role of a Data Scientist is 900k in India.

The Data Science field is very vast and the application of the field is endless, we can make small business predictions to applications that can go for self-driven cars.

Even job opportunities also increased but still lack of effective or skilled Data Scientist. One of the major drawbacks is without any proper planning people are starting this career.

Skills required for Data Scientist

In this article, we are talking about the golden 10 must-have Data Science skills

1.      Fundamentals of Data Science

2.      Statistics

3.      Programming

4.      Data Analytics & Visualization

5.      Machine Learning

6.      Deep Learning

7.      Model Deployment

8.      Big Data

9.      Communication Skills

10.    Out of Box Thinking

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1. Fundamentals of Data Science

Majority of them learning everything online, even newcomers also starting with machine learning without having any clear idea of the basics.

First, need to understand some differences between,

1.      Machine learning Vs Deep Learning

2.      Data Science, Business Analytics, and Data Engineering

3.      Programming tools

4.      Supervised and Unsupervised Learning

5.      Classification vs regression problems

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2. Statistics

Statistical knowledge is a must in the Data Science field, When you are developing models, analyzing data, visualization, etc.. need to use appropriate statistical methods.

It’s starting from the basic like mean, median, mode, normality checking, all types of parametric and nonparametric methods, time series analysis, regression analysis, etc…

3. Programming

Without programming, you can’t be a great Data Scientist. Programming knowledge helps you to communicate with machines. Based on interest and popularity you can choose your programming language.

Some of the commonly used languages are Python and R. Many other tools are available, you can explore it.

4. Data Analytics & Visualization

Always you won’t get cleaned data for analysis and model building, Yes it is dirty, so need to learn data manipulation, and wrangling, this process will take time to understand but will help you in making better data-driven decisions.

Need to understand more on missing value imputation, outlier detection, correcting data types, scaling, and transformation, etc…

For example, if you are doing clustering without scaling maybe leads to inappropriate decisions.

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Data Analysis and visualization where you will understand more about data.

5. Machine Learning

One of the important skills for Data scientists to have is Machine learning knowledge.

Mainly used in predictive modeling, kind of Random Forest, Naïve Bayes classification, etc…

Obviously, you can start with machine learning with linear regression and learn more about Random Forest, XGBoost, SVM, etc..One of the best way to learn this thing just hands on it.

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6. Deep Learning

In the beginning, we talk about self-driven cars all based on deep learning and artificial intelligence.

If you want to an expert in these area’s advanced programming knowledge is must.

We prefer python for the same and if you are concentrating more on statistics and data analytics R will be more ideal.

7. Model Deployment

Model Deployment usually done by machine learning engineers but this will change according to the organization you are working in.

8. Big Data

In daily we are generating a huge amount of data in different platforms like social media, organization level, etc..

Handling a huge amount of data is not easy, need to store it properly and analyze it effectively when needed.

Some of the major tools are Hadoop, Spark, Apache Storm, Flink, etc…

9. Communication Skills

Not in Data Science every field communication skill is extremely important, coming to Data Science the quality of a great data scientist is to convey the problem statement. 

The stakeholders tell their requirement or objective at the beginning of the project and finally Data Scientist need to formulate a problem statement.

10. Out of Box Thinking

Always think from different perspectives and look at the data in different ways, so you can make business profit-driven decisions.

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