Top 6 things to Learn to Become a Data Scientist in 2021

In order to excel as a data scientist, you need to be an expert in the tasks you are required to regularly perform. The below-given article discusses the various skills with their applicability in the firm where you work. They are the best skills that will help you fly up high in your career.

1. It is essential to understand programming in order to become a data scientist. This is because he is required to develop systems and algorithms so as to sieve in the scores of data for the monetary success of the organization. In order to achieve this, it is essential to be knowledgeable of programming.

Knowledge of Python — Python is the most preferred and popular language for data scientists. It acts like an object-oriented language with many data libraries such as NumPy, Pandas, SciPy, TensorFlow, Seaborn, Matplotlib, etc. It is this feature that enables developers to code only with the help of the already known code bases. Thus, he doesn’t need to completely rewrite functionality explicitly. Due to this that the job of developing data applications turns out to be easier. Also, it all comes free of cost. It is the presence of active user and developer community; Python is a great way to win in the field of data science.

Knowledge of R language — R is a programming language with functionalities similar to Python. It is not with such huge support but is many times applied for entirely statistical programming.

Knowledge of SAS — In case when you are working with a big company there always exist chances that you would have to get to know SAS. It is an expensive software suite and has an in-built GUI. This turns it easier to use for people who are not programmers themselves.

2. Being in love with mathematics — You need to remember what you learned in high school in mathematics. As a data scientist, this basic knowledge of Maths will be helpful, that is:

· Probability

· Statistics

· Algebra

· Calculus concepts

You should renew your knowledge again in these mathematical forms and figures. This will help you in your data science career.

3. Data analysis: A specialized knowledge — Assimilation and storage of scores of data is called big data. A data scientist is required to create models that enable in acquiring as well as analyzing it so as to develop meaningful solutions and models. This area of big data application development needs thorough knowledge in Sequential Query Language (SQL that permits algorithms to call and acquire data in specific formats by the application of queries) or Hadoop (which is a software library that enables the distribution of big data in the cluster of computations, for improvement in analysis). Spark can also be applied in addition to Hadoop for the purpose to work with huge unstructured data.

4. Skills for storytelling — It is not only the collection and analysis of data but there is more work to it. As a data scientist, you need to process meaningful outputs from the data and thereby present these findings in a form that can be understood and can become usable for the stakeholders. This is the reason why data scientists are required to have included storytelling methods like data visualizations, this also confirms that the results are well presented. There are several data visualization tools like Ggplot, Matplotlib, and D3.js, etc. In order to be a successful data scientist, you must know very well at least one of these data visualization methods. This will thus help you shine in the data science industry.

5. Machine learning: Knowing and deploying — Knowledge of machine learning and deploying skillfully is a mandatory requirement from a data scientist. When you are working as a data scientist you are required to handle huge data in several formats, and this includes structured and unstructured formats. Machine learning enables you to develop algorithms that will efficiently sieve through and help make predictions based on the large data sets. Therefore, in order to become a data scientist, it is essential to know very well machine learning concepts.

6. Expert knowledge and information of the business — It is required from the data scientist to come up with solutions related to the business using user data. However, in order to come up with these solutions efficiently, you must have a thorough knowledge of the business requirements and the issues which need to be resolved using big data. It is then and mainly then that you can come up with sound and effective solutions. This can strengthen your skills in addition to the data science certifications.


The above information has presented the knowledge area that will help you stand as an expert in a data scientist career. Your success will be profound and you shall increase in worth and value each day due to having acquired the above skills.

Complete content find here:




AI Researcher, Writer, Tech Geek. Contributing to Data Science & Deep Learning Projects. #coding #algorithms #machinelearning

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Big Data — Is it the Gold Mine of Opportunities?

Debunking Skin Motion Tattoo

Market Segmentation with R (PCA & K-means Clustering) — Part 1

Integrating Datadog with SignifAI’s Artificial Intelligence and Machine Learning

Calculating Resistance and Pivot points with Python

Community Detection with Node2Vec

Lessons Learned from Teamwork

Lost and Found

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Albert Christopher

Albert Christopher

AI Researcher, Writer, Tech Geek. Contributing to Data Science & Deep Learning Projects. #coding #algorithms #machinelearning

More from Medium

How to become freelance data scientist while studying?

How to become freelance data scientist while studying?

5 Things I Did to Get My First Data Analyst Job — Without Much Technical Experience

So…I’m gonna become a data scientist. In a month😱

My 2022 Career Goals