8 In-Demand Data Science Skills: Every Data Science Aspirant Must Have

data scientist career Dec 29, 2022
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Want to become a Data Scientist? Wondering what skills are exactly required to be a Data Scientist? 

As the importance of data science continues to grow, so does the need for skilled, competent data scientists. 

Whether you're just starting out in your career or you're looking to make a change and take on new challenges, becoming a data scientist requires a solid understanding of the skills that today’s hiring managers are exactly looking for.

In this article we will look into the following

Why Discover the In-Demand Skills required to be a Data Scientist?

The problem is with everyday new technologies coming up and the organizations using Data Science adopting a breadth of technologies makes it difficult for you to know what exactly is required to become a Data Scientist in today’s Data Science world. 

At Big Data eLearning, we understand the problem and we did gather a lot of stats, reports and thus have the insights, resources, and training that Data Science aspirants like you need,  to get started on your data science journey. 

In this comprehensive article, you will discover the 8 In-Demand skills required to be a data scientist and you will find some tips and guidance on how you can start building those skills today. 

Let's get started!

Becoming A Data Scientist: The Technical Skills

The cornerstone of a successful data science career is technical knowledge.

In today's fast-paced, technological world, it's crucial to have the skills and expertise necessary to work with complex data sets and extract meaningful insights from them.

Let's take a look at some of the technical skills required for a successful career as a data scientist in more detail. 


1) Data Science

At the very core of data science is the ability to collect, aggregate, analyze, and interpret large amounts of data. 

There are around 347K Data Science jobs in LinkedIn as of the beginning of 2023.  One of the common must have skills in all these postings is Data Science.

The surge in demand for data science professionals is accompanied by attractive data science salary packages. The allure of a career in data science is further enhanced by the promising data science average salary, with the average salary of a data scientist often surpassing $120,000 per year

 So “Data Science” tops as number one in our list that we have, as one of the most essential skills required to be a Data Scientist. 

 Data Science skills needed for qualification of data scientist career include:

  • Data Science Basics : Understanding Data Science basics like bias-variance trade-off, in-sample/out-sample
  • Applied Statistics Concepts : Strong knowledge on Applied Statistics concepts like Confusion Matrix, Hypothesis Testing, Probability Distribution, and Chi-square test
  • Data Preparation : Ability to work on data preparation steps like train-test split, imputing
  • Feature Engineering : Competency with a variety of feature engineering techniques such as normalization & standardization, and encodingFeature engineering is working on features, which are nothing but the column attributes of the table data.
  • Metrics : Understanding of metrics like MAE, MSE, RMSE to perform the model selection and evaluation.  These are different metrics that indicate how effective a model is. These metrics will help you to choose the best model.
  • Feature Importance : Familiarity with Interpretation techniques such as Feature Importance, SHAP is also important for learning the nuances of Data Science. Interpretation techniques will help you understand how a machine learning algorithm arrived at a specific decision or prediction

While the above list provides a great starting point for building your data science skill set, it's important to note that these are just the basics. As technology and data continue to evolve, so do the skills required to succeed in this ever-changing field. 

If you are struggling to find what to learn in Data Science, and trying to keep a handy roadmap with the exact skills that are required to get into a Data Science career, look no further.  


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2) Python Programming Language

 Although there are many programming languages like Python, R, SQL, Java, & Scala that are used in the Data Science field, Python is very popular and used by many organizations investing in Data Science. 

According to tiobe-index , Python has been the most popular language consecutively for the past 2 years.  

With its simple syntax, powerful libraries, and a robust ecosystem of tools, Python is an excellent choice for working with both small and large datasets, especially when it comes to Data Science.

Python is clearly the winner, and it comes 2nd in our list when it comes to Data Scientist skills needed.

Python is a versatile programming language that can be used for a variety of purposes, from web development to scientific computing. In the context of data science, Python is often used for data analysis, data mining, and machine learning.

 Some of the advantages of Python for data science include:

  • Python is easy to learn. The syntax is straightforward and there are a number of resources available at Big Data eLearning to help you get started.
  • Python has powerful libraries for data analysis and machine learning. These libraries make it easy to perform complex operations on large datasets.
  • Python has a large community of users. This means that there are plenty of resources available online, and you can find help and support easily if you need it.
  • Python is cross-platform. Programs written in Python run on Windows, Mac OS, and Linux with no changes required. This makes it a great choice for working with data in the cloud or on remote servers. 


3) Machine Learning

Machine learning is a subset of artificial intelligence that involves using algorithms and statistical techniques to enable computers to "learn" from data and make decisions in real time. 

Machine Learning is a subset of Data Science and is mainly focused on building the Machine Learning models and fine tuning it to make predictions and thereby Machine Learning stands 3rd in our list of skills required to be a Data Scientist.

 Some important concepts and skills in machine learning include:

  • Supervised learning, which involves training a computer model using labeled data, such as images or text. The goal of supervised learning is to build algorithms that can recognize patterns and make accurate predictions.
  • Unsupervised learning, which involves training a model on unlabeled data and identifying hidden patterns within the dataset. This approach is valuable for uncovering insights that would otherwise be difficult or impossible to discover.
  • Deep learning, which uses artificial neural networks to mimic the way humans learn. Deep learning is particularly effective for tasks such as image recognition, natural language processing, and speech recognition.
  • Reinforcement learning, which involves training a model with feedback in the form of rewards or punishments. This approach can be extremely powerful for autonomous systems that need to make decisions on their own in complex environments. 


4) Tableau

Tableau is a data visualization tool that is widely used in the field of data science. 

With Tableau, it is easy to create interactive visualizations and dashboards that help you make sense of complex datasets. 

On top of that Tableau has a lot of supported connectors (https://help.tableau.com/current/pro/desktop/en-us/exampleconnections_overview.htm) which can easily connect to many distributed storage systems and perform visualizations without any size or limit constraints.

Tableau can perform visualizations on not only small datasets,  but also on bulky datasets which makes it unique.

Although “Power BI” is another popular visualization tool that is also widely used, tableau is more robust especially when it comes to large datasets on distributed storage systems.

When we compared the interest over Tableau vs PowerBI in Google Trends, on average Tableau is very popular and always in demand.

Please see below the image, where “red” color indicates Power BI and blue color indicates the tableau. 

You can see the search demand for tableau is way higher on an average, which means more people are searching for “tableau” than Power BI, because of the high demand for the Tableau.

So Tableau stands as 4th in our list of skills required to be a Data Scientist.

 So we recommend equipping your skill sets with Tableau which is more in demand on the Data Science landscape.

 Some key features of Tableau include:

  • Flexible reporting options. You can easily filter and pivot your data to highlight key insights, and you can easily share your reports with others.
  • Powerful data connectors. Tableau supports a wide range of data sources, including big data platforms like Hadoop and relational databases like SQL Server. This means that you can easily connect to virtually any type of data source and analyze it in one place.
  • Customizable dashboards. With Tableau, you can build dashboards and visualizations that meet the specific needs of your team or organization.  This makes it easy to share information and collaborate with others on your data science projects.
  • Ease of use. Tableau is feature-rich and simple enough that even beginners can start using it immediately.


5) AWS

AWS is a pay-as-you-go on-demand cloud computing platform.

The world is moving in mass toward cloud technologies. Companies have realized that it’s easier to use managed services like Amazon AWS Cloud, Azure, or GCP Cloud to leverage their cloud computing and storage needs. 

Having knowledge about cloud technologies makes you more valuable and allows you to stand out from the crowd.

Although Azure and GCP are equally popular and growing, we recommend AWS , as It is relatively very easy for a beginner to get accustomed with a lot of free resources. It has the most amount of documentation. AWS has a broad community helping each other. 

Having said that, AWS is our 5th most recommended skill set to have as the skills required to be a Data Scientist


6) Apache Spark

Before you make any predictions in a Data Science project, you need to extract and process a humongous amount of data, as it is the first & foremost process in the Data Science projects.  

For extracting and processing huge amounts of data, there are few distributed storage frameworks available that process across a cluster of computers. 

Hadoop is the pioneer & popular distributed storage framework that was introduced around 2006 until Apache Hive was introduced around 2010.  

In Apache Hadoop Framework, the MapReduce programming model was used to perform the application code.

When Apache Hive became popular in 2010, it provided a SQL way to write the application logic, which prompted many organizations to include Hive in their list of technology stacks.

Apache Spark was introduced around 2014 and it quickly became predominantly popular and is now the best option for distributed processing framework for any organizations.

Apache Spark is a popular big data processing framework that enables you to perform complex analytics tasks on large datasets. 

Per Facebook’s article here , Apache Spark showed a tremendous improvement in terms of performance for their use cases. 

Check the below image, the “orange” indicates Spark, and “blue” indicates Hive.

Less is better. 

In the first metrics, spark spends around 3 times less on the CPU time and around 15 times lesser on CPU reservation time for completing the same query.

 With respect to latency Apache Spark is very quick in returning the query results. It is 2.6x times faster than Apache Hive.

Source : https://engineering.fb.com/

Having said that, Apache Spark is the 6th most recommended skill set to add to your sleeves when working towards a career in Data Science.

Some of the key benefits of Spark include:

  • Speed and scalability. Because Spark is designed for high performance, it can process large amounts of data quickly and efficiently. This makes it an ideal choice for crunching through large datasets in real time.
  • Integration with other platforms and tools. Spark integrates easily with a wide range of big data technologies, including AWS S3 (Storage system), AWS Redshift (Database), Microsoft Azure Storage, Hadoop and NoSQL databases. This makes it easy to combine your data science workflows with other components of your analytics ecosystem.
  • Ease of use. Although Spark provides powerful capabilities and advanced analytics features, it is designed to be easy to use. This means that even beginners can start using it without having specialized training in data science or computer programming. 


7) Apache Hive

Ha ha ha.. Are you wondering why Apache Hive is recommended here, even though we looked at how Apache Spark is faster than Hive, in the previous point?

There are mainly 2 reasons for recommending Hive to add to your list of skill sets.

  1. Hive Metastore is widely used : Even though Spark is faster than Hive, Spark interchangeably is dependent on Hive for certain things like table metastore.  
    • Metastore is nothing but a place for storing the table metadata information.  
    • Metadata is nothing but the data about data.  For e.g. it contains the table name, location, column names, column data types etc..  
    • So a metastore is a place for storing the table name, location, column name, and other metadata details.
    • Spark is a processing framework and the data to be processed can reside in the storage systems like Hadoop, S3, or Azure storage.  However the metadata details are stored in hive metastore.  This is because , for fetching metadata related details, it needs faster access and thereby hive metastore is ideal in this. 
  2. By Learning Hive, you also learn SQL : Organizations where employees are not so tech savvy or organizations where most of the employees are well versed more in SQL, usually rely on Hive for their big data analytics use cases.  
    • By learning Hive, you will learn a lot of SQL concepts like CREATE , INSERT , UPDATE, DELETE, DROP statements, and different types of joins, aggregations, higher order functions etc.
    • Having said the importance of the Hive, Hive is 7th in our list of skills required to be a Data Scientist.
    • Around 37,129 companies are using Apache Hive according to stats by  https://discovery.hgdata.com/product/apache-hive 
    • Apache Hive is a popular data warehouse and data management tool that is commonly used in the field of big data analytics. Some of the key benefits of using Hive include:
      • Flexible querying capabilities. With Hive, you can easily query your datasets using SQL-like commands, which makes it easy to explore and analyze your data.
      • Ease of use. Although Hive provides advanced querying and data management features, it is designed to be easy to use for beginners. This makes it ideal for analysts who are just getting started with big data analytics.
      • Extensibility. Hive integrates easily with other tools and platforms, including Apache Spark and SQL Server, which allows you to combine them with the other tools in your data science workflow.


8) Linux and Airflow

Linux is an open source operating system that manages system resources like CPU, memory, and storage. Linux commands are essential for any Big Data or Data Analytics.  By learning Linux you get a stronghold on the file system commands.  This is especially useful for debugging, analyzing the files and file system. 

These are also helpful in debugging the Virtual machine containers and network operations.

Airflow is a workflow automation tool that helps you schedule and execute complex data processing tasks. It is commonly used in the field of big data analytics. 

Once you have extracted, transformed, analyzed, & parsed the data, you will apply the machine learning models to extract insights out of the data.

 However managing and orchestrating all these different tools in a pipeline is difficult. This requires too much manual effort. Airflow helps in orchestrating the different processing seamlessly.

Airflow offer key benefits, including:

  • Flexibility and Scalability. Airflow makes it easy to schedule and execute complex tasks across your cluster. These features enable you to process large datasets quickly and efficiently.
  • Integration with other tools. Airflow offers a wide range of integrations with other big data technologies, which makes it easy to combine them with the tools you're already using in your data science workflow.

Overall, if you're looking for a powerful platform on which to orchestrate your big data analytics applications, Airflow is a great option that can help you get the results you need.

Bonus : Becoming A Data Scientist: The Soft Skills

To be successful as a data scientist, you need more than just technical skills. In order to thrive in this field, you also need strong soft skills that enable you to collaborate with others and communicate effectively with your team and stakeholders. 

Let's take a look at 3 essential soft skills you need as a data scientist.

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#1: Business Domain Knowledge

One of the most important soft skills for data scientists is the ability to understand business domain knowledge. 

This means having a strong understanding of the industry that you're working in, including key concepts and terminology. 

By deeply understanding your industry, you'll be better equipped to identify business opportunities and deliver actionable insights that can drive real results. 

Having knowledge on how Data Science solves business problems in Retail, e-commerce, Communication, Social media, Digital Streaming, Search, Email service providing companies is crucial to become a Data Scientist.


#2: Teamwork Skills

In order to succeed in data science, it's also essential to have strong teamwork skills. This includes being able to collaborate with your team members effectively and communicate your ideas clearly and efficiently. 

Now imagine you are hiring a candidate for your team. 

Would you choose a person who is extremely tech savvy but jerk and works individually without talking to anyone in the team, 


would you rather choose a person who has the needed knowledge and skills, but also is very amicable and works greatly together well with the team.  

You would choose a person who works as a team, right? :-)


By working well as part of a team, you'll be able to share ideas and resources, which can help you achieve your goals more quickly and easily. 

Additionally, having strong teamwork skills can also be important for your career development. Many data science roles require working as part of a team, so the ability to collaborate effectively will likely help you stand out from other candidates and advance in your career.


#3: Passion for Data Science

In order to be successful in data science, it's also important to have a strong passion for the field. 

This means being excited about the opportunities and challenges that come with working with big data and always looking for ways to improve your skills and stay up-to-date with the latest trends in data science. 

No doubt about your passion as you have come this far through this article :-) 

By having a true passion for data science and constantly working to better yourself as a data scientist, you'll be able to stay motivated and succeed in this exciting field.


Whether you're just starting out in your career or looking for a change, a career in data science can be an exciting and rewarding option.  

But before pursuing this path, it's important to make sure that you're ready to succeed as a data scientist. 

Some key considerations include having strong technical skills, in 

  1. Data Science, 
  2. Python, 
  3. Machine Learning, 
  4. Tableau, 
  5. AWS, 
  6. Apache Spark, 
  7. Apache Hive, 
  8. Linux and Airflow will go a long way in getting into the Data Science career.  

 Also acquiring the 

  1. Business domain knowledge, 
  2. Working well as part of a team, and 
  3. Having a passion for data science will make you stand out from the crowd. With these additional soft skills in place, you'll be well-prepared to pursue a career in this dynamic and fast-growing Data Science field. 

Are you ready to take your data science skills to the next level..?


 Are you someone looking for a roadmap to get into a career in Data Science..?

It all starts with having a proven roadmap in hand..

BigDataElearning's "The Data Science Aspirant's 90-Day Proven Roadmap" is exactly what you will need.

What does the Roadmap contain ? 

Data Science Skills & Tools : Roadmap jots down the skills and tools you will need to learn in Data Science, so that you don't need to be in an endless guessing game of what today's data science hiring managers are looking for..

Exact Topics for Each Skills : Roadmap also lists the exact topics within each Data Science skills that you will need to know, so that you don't need to spend endless nights learning topics that are not actively being looked by today's data science organizations..

Links to Resources : The roadmap contains links to resources where you can learn with fun, so that you don't need spend time searching for resources only to find later that they are boring

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Follow the roadmap to become a successful Data Scientist...Good luck...

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Now About You!

Are you already a Data Scientist? 

If you are, leave a comment below with “yes”. 

If you are not, leave a comment with no.

If you answered “no”, what is the biggest problem you are facing from getting into a Data Science career?


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