Top 14 Skills for Data Scientists to Master in 2020

Introduction

With data science taking over the corporate world, everyone is eager to learn the top skills for the Data Scientist job profile. There are 2.5 Quintilian bytes of data created each day, and companies require professionals who can convert this data into insights and utilize it to generate profit.

Data Scientists are always on demand as businesses encounter complexities that can be only resolved by efficient data analysis. There’s no doubt in asserting that data science has become the core component of businesses as it enables them to make well-informed decisions based on statistical data, trends, and numbers.

To become an expert in the domain, you need to master the skills required for Data Scientist positions in various companies and organizations. So, let’s take a look at the must-have skills for Data Scientist jobs.

1. Fundamentals
2. Statistics
3. Data Visualization
4. Data Ingestion
5. Data Munging
6. Data Manipulation
7. Data Integration
8. Programming
9. Machine Learning (ML)
10. Deep Learning
11. Data Science Tools
12. Big Data
13. Problem Solving
14. Soft Skills

Let us take a deeper look at what the Top 14 Skills for Data Scientists to Master:

1. Fundamentals

Having a strong hold on the basic concepts and fundamentals is one of the primary skills for Data Scientist job profiles. The fundamentals include proficiency in Matrices & Linear Algebra Functions, Hash Functions & Binary Tree, Relational Algebra, Database Basics, Extract Transform Load, and more.

2. Statistics

Statistics is one of the essential skills required for Data Scientist jobs. This is one of the key data science skills that enables you to collect, organize, analyze, and interpret data. This includes Descriptive Statistics (Mean, Median, Range, Standard Deviation, Variance), Exploratory Data Analysis, Percentiles and Outliers, Probability Theory, Bayes Theorem, Random Variables, Cumulative Distribution function (CDF), Skewness, and other Statistics fundamentals.

3. Data Visualization

Data Visualization is a graphical representation of data. It is an integral part of the data life-cycle. Having good hands-on knowledge and experience in it is one of the vital skills for Data Scientist positions. A few visualization tools to master are Tableau, Kibana, Google Charts, and Datawrapper.

4. Data Ingestion

Data Ingestion is the process of importing, transferring, loading, and processing data for later use or storage in a database. It involves loading of data from various sources. Being able to perform Data Ingestion is one of the most essential Data Scientist skill sets you need to become a Data Scientist. Apache Flume and Apache Sqoop are two most popular data ingestion tools you would need to master.

5. Data Munging

It is the process of making raw data clean enough to use it as input for the analytical algorithm. It is one more important part of the data life-cycle. You can use R or Python Packages for Data Munging.

6. Data Manipulation

One of the essential skills for Data Scientist is Data Manipulation. It includes the process of changing and organizing data to make it easier to read. It uses Data Manipulation Language (DML), a programming language that adjusts data by inserting, deleting, and modifying data to map it.

7. Data Integration

It is the process of combining data residing in different sources and providing a unified view of them. It is one of the most important skills for Data Scientists to have hands-on experience. Data integration is vital for organizations as it allows them to analyze data for business intelligence. Thus, being equipped with Data Integration will allow you to land a Data Science job in a reputed organization.

8. Programming

It is one of the must-have skills required for Data Scientist jobs. When it comes to programming for Data Science, Python is one of the most sought-after languages. Along with Python, R is also another popular programming language you must be proficient in.

9. Machine Learning (ML)

For companies that manage and operate on vast amounts of data, and function on a data-centric decision-making process, Machine Learning is an add-on to the Data Scientist skill set. ML is a subset of AI that contributes to the modeling of data. It uses algorithms like K-nearest neighbors, Random Forests, Naive Bayes, Regression Models.

10. Deep Learning

It is an advanced form of Machine Learning. Nowadays, every organization is deploying Deep Learning models as it possesses the ability to solve limitations of traditional Machine Learning approaches. Other skills for Data Scientist jobs include fundamentals of Neural Networks, the library used for creating Deep Learning models like Tensorflow or Keras, and how Convolutional Neural Networks, Recurrent Neural Networks, and RBM and Autoencoders work.

11. Data Science Tools

To get a job as a Data Scientist, you must have hands-on experience with most-sought-after data science tools such as MS Excel, Python or R, Hadoop, Spark, Tableau, and more.

12. Big Data

Big Data is becoming a popular buzzword in the Data Science domain. It has become a crucial need for companies as it aids in improving business decision making and provides them an edge over the competitors. 

13. Problem Solving

One of the inherent skills required to become a Data Scientist is having an appetite for solving real-world problems. A Data Scientist needs to productively approach a problem, which means you must develop the art of calculating the risks associated with specific business models.

14. Soft Skills

Having good soft skills is one of the critical skills required to make a career in Data Science. Companies value soft skills in a potential Data Scientist because it helps in understanding the business requirements or the problem at hand, and persuasively communicating insights to the stakeholders.  

Conclusion

These are the main skills required for Data Scientist job profiles. Data science is a constantly evolving field, and it is very important to keep updating your data science skills to become an expert in the domain. 

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