Data scientist is one of the highly trending designations, IT professionals aspire to transit towards. Not to mention the hefty salary package it comes with, aside from that there’s a certain roadmap one needs to follow to strengthen their chances at being selected. To become a competent data scientist in 2021, these skills are prerequisites or data scientist qualifications that need to be honed beforehand.

1. Statistics

2. Python/R (Programming language for coding, and analysis)

3. SQL (Structured Query Language for transforming databases and saving programming time with the use of concise commands)

4. Machine learning and Artificial Intelligence (Algorithms and data usage to mimic how humans learn. Concepts include time series, reinforcement learning, outlier detection, supervised and adversarial learning, computer vision)

Enrolling in a data scientist course would assist the aspirants to not only gain technical knowledge but also get certificates upon completion of their syllabus.

Technical knowledge for becoming a Data scientist

1. Calculus & Linear algebra  (for building machine learning models, plotting and finding function values)

2. Statistics (for insights related to actionable data, plus exploring and understanding data precisely. Checking data anomalies)

3. Probability (for doing further analysis and estimation, example: identifying variable relationships and dependencies, future trends prediction)

4. Data wrangling (presenting data to businesses for taking action on)

5. Programming language (Julia, Python, R, TensorFlow, Scala, SQL)

6. Data visualization (via pie charts, bar charts, scatter plots)

7. Data management (via Oracle, MySQL)

8. Soft skills (improving interpersonal and communication skills for taking project responsibilities)

9. Business acumen (gaining proper knowledge about the concerned sector, understanding business issues to be resolved)

The right kind of intuition to understand the problem and building solutions creatively is a must.

Some more Data Science skills to master

1. Data cleaning

2. Data ingestion

3. Data analysis (Exploratory)

4. Data pre-processing

5. Decision making

6. Identifying data patterns

Common Data Science Interview Questions you must have answers to

While cracking the interview stage shouldn’t scare you, nonetheless, you should brush up on the basics, as in with data scientist interview questions, you could be asked to explain these concepts or interrogated about some of them.

1. Standard deviation and variance

2. Bayesian statistics

3. Mean and median

4. Correlation and covariance

5. Hypothesis testing

6. P-value

7. Selection bias

8. Confusion matrix

9. Format data (Long and wide)

10. Confidence interval & point estimates

11. Probability-based prediction questions

12. Regularization

13. Cluster sampling

14. Cross-validation

15. Decision trees

16. Collaborative filtering

17. Ensemble learning

18. Weights initialization on network

19. Hyperparameters

Keep in mind these are random questions from different components of data science including Statistics and machine learning that can come your way. Having the answers handy would boost your impression of the interviewer and maximize your chances at the selection.

Start Learning Data Science

Different aspects of data science

The data scientist career path involves:

1. Technical facets

2. Non-Technical aspects

Both of which are equally important to make you the go-to candidate. You cannot rule out the ability to explain concepts in laymen’s language to clients. In 2021, you will require to master both of these to progress further in your quest to becoming a data scientist.

Start Learning Python Programming

Get a grip on these

1. Machine learning algorithms (Applying and implementing in the program)

2. Cloud platforms (example: Azure, Google, AWS for the deployment of models)

3. Apache spark (Computation framework for carrying out analytics to distributing company from data intake)

4. Logistic regression & decision trees (Solving problems on the basis of organizational outcomes)

5. Unstructured data (Non-streamlined data that doesn’t fit within database tables, manipulation of this provides insights for decision-making)

Stand out with your Data Science resume

To be noticed fairly easily, it’s important to consider keeping your data scientist resume up to date. There are some key components you will need to ensure. These include:

1. Contact (give correct information)

2. Education (mention educational qualifications)

3. Skills (list the  concepts you have mastered)

4. Work experience or projects (write down any relevant industry exposure)

5. Proper formatting (keep it brief, accurate, and bulleted)

6. Personal hobbies (share a little personal about your likes and inspirations)


Learning everything by yourself, all alone can seem like a daunting task, but opting for a course that provides data scientist certification will smoothen your IT educational & professional journey. Furthermore, the institute will introduce or encourage you to join data science communities online would keep you afloat with the latest trends in this industry.

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