According to the recruitment firm Michael Page’s 2021 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?
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 the skills required 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 675% rise in the search volume for the term “Data Scientist”, 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,
- Machine Learning Engineer,
- Data Product Owner,
- Big Data Engineer
Then what would you do in their shoes? Let’s find out here:
- Identify the Data Analytics problems
- Determine the correct Data Sets and variables
- Collect large amounts of data
- Clean and validate data
- Analyze the data
- Data Mining
- Interpret data
- 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.
Skills required to be a Data Science Professional
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:
- Identifying & breaking down the Problem into understandable chunks.
- Extracting Data.
- Data Cleaning.
- Testing Data Analysis.
- Feature Selection.
- Incorporating Machine Learning Algorithms.
- Testing the Models.
- 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:
- Machine Learning Libraries
- Data Transformation
- Statistical Packages
- Greater control over data
- Version Control
And which programming languages do you need to know?
- Structured Query
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.
So, do you have the Data Science skills to get started? Not sure, an Expert opinion might help! Consider, talking to Experts with Zen Class GUVI.
You can start learning Data Science from scratch from IIT-M CEE Experts and Subject Matter Specialists at Zen Class. Check here.