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The Most Important Skills Needed to Become a Successful Data Scientist in 2021

This article was published as a part of the Data Science Blogathon.

In the present era, plenty of aspiring data science professionals are trying their best to switch to a data science career, hence the competition has become really tough. In today’s article, I shall be discussing the necessary skill sets needed to become a successful data scientist in 2021.

Prerequisites Skill Sets Needed to Become a Successful Data Scientist

  1. Python/R

  2. Machine Learning

  3. SQL

  4. Statistics

These skills are absolutely necessary for today’s era to step into as a data scientist. I will be explaining how we can make the best of these skill sets to ace your data science journey.


Mastering A Programming Language is Absolutely Necessary


data scientist skills programming language

No matter which background you hail from, it is really important for us to learn and master one programming language to solve machine learning-based problems and give a possible solution. I recommend Python as it is easy to learn and has plenty of libraries like pandas, Keras, spark, etc which helps you in building the machine learning models for your project. If you are from a software background then languages like Java, Ruby, Julia, C++ can also be implemented to your machine learning models. However, Python is easy to learn and is a high-level programming language, & the python libraries are continuously evolving to adapt to the current user requirements in terms of building the model or analyzing the dataset effectively.

We must learn the skill sets in such a way that it helps us in honing our knowledge. Basic Knowledge of database language like SQL is important to help you pull the relevant information from your dataset.


Learning these Skills Are Important to become a Successful Data Scientist

Did you know that data science professionals spend around 60 percent of their time working on their datasets? The majority of the work done by you will involve:

Data Scientist skills process


  1. Data Ingestion

  2. Data Pre-Processing

  3. Data Cleaning

  4. Exploratory Data Analysis

  5. Feature Engineering


Unlike datasets found in platforms like Kaggle, while working on real-world problems the dataset will not be easily available. Hence, you must extract the data carefully and once you get your hands on the data, it is important to preprocess the data and clean the data to meet your requirements.

Hence, you should be comfortable in performing Exploratory Data Analysis before moving any further. If you miss even a single step while processing the data, you won’t get desirable results and it would thus incur huge losses. Instead of focusing on building the models, we should rather focus on identifying some patterns in the data if it’s readily available to make an informed decision.


Intuition & Creativity to be actively applied to become a Data Scientist

Yes, you heard it right, having the right intuition to understand the problem and building possible solutions to meet the client requirement requires a lot of experience and presence of mind. It’s not just like data entry work wherein you don’t have to take stress while working. Instead, we should include a pinch of creativity to think about all the possible solutions and different approaches that can be used to start building the model.

Well, Data Science is all about mixing all the required tools together to get the job done. As a data scientist, you are required to extract the necessary knowledge from the data to solve the questions and problems put forward by the clients. Now, we do know that we don’t need to learn anything and everything but in 2021 we will need to learn both the technical and non-technical skill sets in order to be successful.


Technical Skillsets Needed to become a Better Data Science Professional:

1. Statistics & Probability

  • To gain actionable insights from data it is very important to have sound knowledge in statistics and probability. They help in making estimates for further analysis.
  • With statistics & probability you and explore and understand the data in a better fashion

  • Identify the dependencies and relationships that exist between the variables

  • Predict the possible future trends based on past data trends

  • Identify any existing patterns in the data

  • Check anomalies present in the data

  • Statistics are a crucial part of data-driven companies wherein they depend on the data to evaluate the data models.

2. Linear Algebra & Calculus

  • Most of the machine learning models are built with several unknown variables. Hence, having sound knowledge of calculus is crucial for building a proper machine learning model. We have listed down few topics that help you in building a workable model:
  • Gradients & Derivatives

  • Sigmoid function, ReLU (Rectified Linear Unit) function,Step function,Logit function

  • Cost function (It is important)

  • Plotting the functions

  • How to find Maximum & Minimum values of a function


3. Programming Language

It is important to have proper knowledge in coding and programming. Having programming skills will help you transform the raw data into proper insights. Although as an experienced, programmer you can choose any language to build models, but in the current scenario, aspirants from non-technical backgrounds and beginners are preferring languages like Python and R due its simplicity and ease of use.

Following are the most popular programming languages which will fit right with your Data Science Skillsets:

  • Python

  • R

  • SQL

  • Julia

  • Java

  • Scala

  • TensorFlow

It is preferred if you learn the basics and nitty-gritty of a programming language before trying to build a model. While programming you will come across a lot of errors, you need to have the apt skill sets to identify the same and rectify the same.


4. Data Wrangling

It is common practice in real-world scenarios, where the actionable dataset is not in proper format as intended by the businesses. Hence, it is important to know the right processes to deal with the anomalies in the data. With data-wrangling, you can actually prepare your data by cleaning the data and transforming the raw data to a form that provides in-depth analysis for further insights.

With Data Wrangling, you can offer an accurate presentation of actionable data to businesses. It also helps in reducing the processing time, & helps you in organizing the unruly data.


5. Database Management

Normally about 60-70% of the work involves pre-processing and cleaning the dataset for further use. At times, we need to deal with heavy data and hence, it is important to know the best way to manage that data. DBMS or Database Management allows you to retrieve, manipulate, edit and transform the required datasets. It also helps us in further testing the data once we have built the model. DBMS like SQL, Oracle, MySQL, Cassandra, MongoDB are some of the popular database management systems used in today’s scenario.


6. Data Visualization

Undoubtedly, data visualization is one of the most important skills that help you understand the data, learn about its various features and represent the results in the end. It also helps in fetching the meaningful details about the data that can be utilized to build the model.

We can perform data visualization through pie charts, scatter plots, bar charts, line plots, heat maps, etc. Tools like Tableau, PowerBI, Google Analytics can help in visualizing the data.


7. Business Acumen

To become a successful data science professional it is important to have proper knowledge about the industry you are working in. It is best to understand the underlying issue and what are the essential business problems that your company wants to resolve. Always take assistance from an industry expert in the said domain to get a better insight and move forward with a solution or a decision that you deem to be fit for the model.


8.Soft Skills

As a data scientist, you are not only responsible for finding accurate solutions to meet the business needs, but also you have to communicate the same details in layman’s language to your company stakeholders, clients, managers so that they understand your approach and try your method. Hence a data scientist needs to hone your communication skills to take up responsibility for certain important projects that are crucial to your company.

Once you have mastered these skills as a data scientist please spend some time mastering machine learning algorithms, implementing the same in the program, learning cloud platforms like Google Cloud Platform, Azure, AWS to deploy the models.

Slowly, plenty of aspirants are trying their hands at the data science career, hence it is really important for us to get the basics right, build a strong foundation and keep learning and thriving throughout our journey. Join, data science communities like Kaggle, AnalyticsVidhya and participate in hackathons to hone your skillsets. Try writing programs and post them on GitHub. Share your knowledge on platforms like LinkedIn and start a healthy discussion with like-minded people from the same background. If you need additional help to ace your game, don’t forget to enroll yourself in data science courses offered by popular companies to get your doubts cleared and learn your concepts properly.

I hope this article gives you the necessary knowledge on skills required to kickstart your career as a data scientist.

All the best!

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