What Skills Are Needed To Be A Data Scientist?

According to the recruitment firm Michael Page’s 2023 India Talent Trends report, data science professionals with around 3-10 years of experience can claim salaries from 25-65 lakh Annual packages, while those with more experience can steer pay packages upwards of 1 crore. Wait! What? I mean are you, like me pondering what exactly Data Scientists have to do? I mean what roles and responsibilities do they fit in? Do you have the Data Science skills to get started? and What skills are needed to be a Data Scientist?

By the way, we have done this small research for you and found that statistics say you can very well acclaim the top Data Scientist skills within just 90 days. I don’t promise that you can go ahead and be a Data Scientist just like that. But of course, you can kickstart a ravishing Data Science career from there.

So, before we jump straight into what skills are needed to be a Data Scientist, let’s understand who is a Data Scientist and what job roles and responsibilities come under the umbrella of Data Science?

Who is a Data Scientist?

Do you know that the Harvard Business Review rightly asserted that “No job would be more sought-after over the next decade than Data Scientist”?

True to the statement, Data scientists are the most sought-after professionals, as of date. If you go ahead and read the Google trends, there is a 75% rise in the search volume for the term “Data Science”, which is more or less just growing every day.

Data Scientists are the professionals who unleash the power of Data. In the process, they analyze problems, collect adequate data, and uncover secrets in those Data to formulate a great solution to the above big problem. 

The market is so buzzed out with Data-related professions, that I am sure you would have heard of all these job roles that you would be open to as a Data Science professional.

• A Data Scientist, 
• Data Architect/ Engineer, 
• Database Manager, 
• Data Analyst, 
• Business Analyst, Statistician,
• ETL, 
• Machine Learning Engineer, 
• Data Product Owner, 
• Big Data Engineer

Then what would you do in their shoes? Let’s find out here:

1. Identify the Data Analytics problems
2. Determine the correct Data Sets and variables
3. Collect large amounts of data
4. Clean and validate data
5. Analyze the data
6. Data Mining
7. Interpret data
8. Communicate findings to stakeholders

Now that we know what jobs and responsibilities await you out there, let’s focus on the skills required to be a Data Scientist.

Before we move into the next section, ensure you have a good grip on data science essentials like Python, MongoDB, Pandas, Numpy, Tableau & PowerBi Data Methods. If you are looking for a detailed course on Data Science, you can join GUVI’s Data Science Course with placement assistance. You’ll also learn about the trending tools and technologies and work on some real-time projects. 

Additionally, if you want to explore more Data Visualization through a self-paced course, try GUVI’s Advanced Data Visualization course.

What Skills Are Needed to be a Data Scientist

Like any other career, the higher your position, the greater suite of skills you’ll need to carry to be successful. However, at an entry-level, there are certain skills you’ll need to be proficient in, and these skills you need to curtain regardless of your role. 

• Statistics & Mathematics
• Analytics & Modeling
• Computing & Programming
• Data wrangling, Data Visualization
• Excellent Communication
• Intellectual Curiosity &
• Machine Learning Know-how

So, the above list includes the data science skills that you should master to be a pro in data science.

So, for that ‘more’, let’s dig down and get to the core requisites of Data Science.

1. Statistics & Mathematics

Can you deny if I tell you there is math in everything around us? Starting from shapes & sizes, weights & measures, distance & proximities, everything makes proper sense with maths! So, data is about these entities & retrieving & delivering it depends majorly on your mathematics skills. Above all, Statistics is a Mathematical Science about data collection, analysis, interpretation and presentation.

So, to process solutions to your Data Science and analysis problems, you should possess a good understanding of statistics. Multiple Statistical functions, principles, and algorithms help in interpreting the raw data. Moreover, building a Statistical Model and predicting the result is much better done with clarity in statistics.

Fundamental concepts like Linear Algebra, Calculus, descriptive Statistics, & inferential statistics are prerequisites to being a Data Professional. So, to be a good Data Scientist, you should be good in Mathematics and statistics.

2. Analytics & Modeling

Data Analytics & Modeling play a vital role in Data Interpretation. And hence, are significant when Data Science into the picture. Analytics and modeling process basically covers the process of developing data sets for training, testing, and production purposes.

Data analytics is the process of studying data sets in order to find trends and draw conclusions about the information they contain. While Data modeling is the process of creating a descriptive diagram of relationships between various types of available information. The primary goal is to create the most efficient method of storing information while still providing complete access and reporting.

Further, the 8 simple steps of Data Analytics & Modelling are:

1. Identifying & breaking down the Problem into understandable chunks.
2. Extracting Data.
3. Data Cleaning.
4. Testing Data Analysis.
5. Feature Selection.
6. Incorporating Machine Learning Algorithms.
7. Testing the Models.
8. Deploying the Model.

3. Computing & Programming

Though not a pro-programmer, every employee would like their Data Scientists to have a basic understanding of programming languages. As a Data Scientist, you should know programming when it comes to Exploring, cleaning, analyzing, and presenting data. Pretty much everything is Data Science.

Basically one needs to have good programming knowledge when it comes to the following implementations:

1. Machine Learning Libraries
2. Data Transformation
3. Statistical Packages
4. Greater control over data
5. Version Control

And which programming languages do you need to know?

• Python
• R
• Structured Query
• Language
• JavaScript

4. Data wrangling & visualization

 Data Wrangling, is the process of cleaning and transforming of one type of data to another type. in short, Data wrangling outputs processed data that can be used for visualization.

Making raw data usable is the main purpose of Data Wrangling & Visualization.

5. Excellent Communication

Talk, understand, conversate, and get the data! No compromise on communication! Like any other profession, a Data Scientist aspirant too must possess the best of it. Both verbal & non-verbal!

6. Intellectual Curiosity

The eagerness to learn and explore everything the key player for progress. The technology-driven platforms demand progressive learning and never-ending curiosity!

7. Machine Learning Know-how

Collecting & preparing the data, Choosing a model, evaluating & training it, parameter tuning & making predictions, etc., are some of the Machine Learning techniques that you should be knowing. In short, Machine learning is a method of data analysis. It automates analytical model building.

Read here: Is Data Science A Good Career Choice?

Wrapping Up:

So, do you have the Data Science skills to get started? Not sure, an Expert opinion might help! Consider, talking to Experts with GUVI’s Career Program.

Kickstart your Data Science journey by enrolling in GUVI’s Data Science Course where you will master technologies like MongoDB, Tableau, PowerBi, Pandas, etc., and build interesting real-life projects.

Alternatively, if you would like to explore Data Visualization through a Self-paced course, try GUVI’s Advanced Data Visualization certification course.