Top 10 must-have skills for data scientists

As we all know data scientists are in huge demand; Not only you but everyone is eager to become a data scientist. And in this throat-cutting competition, only the skilled one will get the right opportunities. 

Businesses encounter various problems in day-to-day business that can only be tackled by skilled data scientists, so in this blog, we will be talking about the top 10 skills you should master if you want to become a data science expert. 

data scientists

 1) Fundamentals of Data Science

Most of the newcomers in the field of data science make a very common mistake. They start applying machine learning techniques right off the bat without even clearing their basics. 

The primary important skill you require is to understand the fundamentals of data science, artificial intelligence, and machine learning as a whole.

Having a strong command of the basic concepts is one of the primary skills for a data scientist.  

This includes:

        Matrices and Linear Algebra Functions

         Binary Tree and Hash Functions

        Database Basics

        Relational Algebra

        Extract Transform Load (ETL)

        Reporting VS Business Intelligence VS Analytics

        Common terminologies and tools

        Regression and Classification problems 

 

2) Statistics 

Statistics is one of the most important skills that you require for Data Scientist jobs.

Statistics


 Statistics enables you to collect, organize, analyze, and interpret data. Important concepts in statistics include:

        Descriptive Statistics (Mean, Median, Range, SD, and QD)

        Exploratory Data Analysis

        Percentiles and Outliers

        Probability Theory and Bayes Theorem

        Random Variables

        Cumulative Distribution Function (CDF)

        Skewness, and other Statistics fundamentals.

 

The knowledge of these concepts of statistics is a must to become a data scientist. 

 

3) Programming knowledge 

Programming languages act as a medium to communicate with the machines. But do you need to become the best in programming? No, not at all. But you should have enough knowledge about coding.

 

First of all, choose a programming language that you like the most. Python, Julia, or R some of the most famous programming languages in the field of data science and each has its own Pros and Cons.

But when it comes to data science, Python is one of the most sought-after languages. Along with it, you must also be proficient in R. 

 

4) Data Visualization 

Data Visualization means the graphical representation of data.

And this is undoubtedly the most fun part of data science. This is the skill that you should truly master because being a data science expert you must know how to build a story out of the visualizations.

To start with you must be familiar with:

        Histogram

        Pie charts

        Bar charts

        Waterfall charts

        Thermometer charts, etc.

 

While the tools to master are Kibana, Tableau, Datawrapper, and Google Charts. 

6) Machine Learning 

Machine Learning is a subset of Artificial Intelligence that contributes to the modeling of data and building predictive models. Machine Learning is a core skill to a data scientist's arsenal. Machine learning uses various algorithms like Time Series Analysis, Naive Bayes, Random Forests, K-nearest neighbors, and Regression Models. 

Data science practitioners need to understand how machine learning and these algorithms work. 

 

7) Deep Learning

Deep Learning is an advanced form of Machine Learning which holds the power to solve the limitations of traditional Machine Learning approaches. 

Smart assistants, self-driving cars all are fruits of deep learning.

You must have good knowledge of programming (especially Python) and a good grip on mathematics and linear algebra. 

Other skills related to deep learning include:

        Fundamentals of Neural Networks

        Libraries like Tensorflow or Keras

        Learning how Recurrent Neural Networks, Convolutional Neural Networks, and RBM and Autoencoders work.

8) Big Data

Big Data is another popular buzzword in the domain of Data Science. Big data provides organizations an edge over their competitors and helps in improving business decision-making.  

According to various surveys, today we are generating around 2.5 Quintillions per day! Due to the rise of the internet, IoT, and social media networks, there has been an unforeseen boom in the rate of data.

Such a large amount of data is overwhelming for organizations and they are trying to tackle this data by adopting Big Data Technology so that this large amount of data can be stored and efficiently used when needed. 

9) Data Integration 

Combining the data residing in different sources and providing a unified view of it is known as data integration. Every Data Scientist should have hands-on experience in data integration. Data integration allows organizations to analyze data for business intelligence. Being well versed with Data Integration will help you get a Data Scientist job in a reputed organization. 

10) Data Munging 

Have ever performed data analysis? then you must have definitely come across the feature selection before applying your Analytical model to the data. In a nutshell, every activity that you do to turn your raw data into clean data is called data munging. Data scientists usually use ‘R’ and ‘Python’ packages to do that.

As a Data Science practitioner, you should understand all the important features in the dataset and remove the unnecessary ones. 

Enough talk about technical skills now let's talk about some of the important soft skills that you must have as a data science practitioner. 

Soft Skills

 

Soft Skills

 

Having great soft skills is critically required to make a good career in Data Science. Soft skills like communication skills, storytelling skills, Structured and creative thinking, are really important if you want to become a data science expert.  

Conclusion 

Being a data scientist in today's time is undoubtedly very rewarding and tonnes of progressions await in the future. And the skills that we just discussed will help you to get the right place in this domain.

But just keep in mind that data science is a constantly evolving field. And believe me, learning never stops in this field. You master a tool and it gets run over by a new tool the very next day. So, it's very important to keep upskilling from time to time and stay updated on the latest trends and innovations. If you want to become a good data scientist then be curious and always keep learning.

Author Bio 

Senior Data Scientist and Alumnus of IIM- C (Indian Institute of Management - Kolkata) with over 25 years of professional experienceSpecialized in Data Science, Artificial Intelligence, and Machine Learning.

PMP Certified

ITIL Expert certifiedAPMG, PEOPLECERT and EXIN Accredited Trainer for all modules of ITIL till ExpertTrained over 3000+ professionals across the globeCurrently authoring a book on ITIL "ITIL MADE EASY".

 

Conducted myriad Project management and ITIL Process consulting engagements in various organizations. Performed maturity assessment, gap analysis and Project management process definition and end to end implementation of Project management best practices

 

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