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An Ultimate Guide To Data Science Career Path In 2021

Data science career path strategy keeps evolving due to the varying demand for organizations. Over the years, aspirants with minimal knowledge could land a data position because there was a dearth of talents. However, today millions of people are learning data science, leading to enough aspirants for job openings. Unlike yesteryears, you might not get a job offer if you struggle to differentiate among other applicants in 2021. To ensure you learn and get data science jobs, you have to devise an effective data science learning path in 2021.

Here is a 21-step data science career guide for 2021:-

  1. Develop Problem Solving Aptitude: More often than not, aspirants try to learn data science because of the hype. As a result, they ignore the skill of developing rigor for solving business problems. You need to have the curiosity to find challenges in day-to-day lives and a passion for solving problems. Either there are shortcomings with the way products and services are delivered or issues that are ignored altogether. When data is everywhere, you should find ways to leverage data science techniques and mitigate pain points for businesses in the digital age.
  2. Learn Structure Thinking Framework: Structured thinking is the art of applying a framework to an unstructured problem to simplify the process by understanding intricacies at the macro level. Beginners often try to fit machine learning techniques into problems from the first go because they lack the ability to think structurally for any situation. Instead, it would help if you mindmap how a problem can be solved from the beginning till the end. This does not mean that you will have a perfect framework, but an overall approach to solving problems will streamline the entire process.
  3. Understand the Basics Of Data Science: When you have a problem-solving aptitude and structured thinking ability, you need to acquire skills to solve problems. For this, you should read several blogs and talk to data science practitioners to understand the scope of machine learning, data, and more. Some problems cannot be solved with machine learning techniques. Consequently, you will know where you can apply data science practices and where you cannot.
  4. Explore Different Domains: This is one of the crucial stages in your data science career path. You cannot master a lot of domains in one go as every sector has its share of challenges that may require a completely different approach. It is recommended to figure out how machine learning is used in other domains; this will give you a heads-up regarding standard practices in specific sectors like BFSI, retail, and more. Besides, if you are passionate about a particular domain, you can even effectively strategize your data science career path from the very beginning.
  5. Learn Programming Language: Unfortunately, most of the aspirants start with learning a programming language. Non-technical aspects like the right attitude, critical thinking, structural thinking, and storytelling are equally important. Make sure that you go through the above four steps before learning a programming language. You can either learn Python or R programming languages to start. But, do not fall for questions like Python vs. R programming for data science. Further, get started with IDE or use Jupyter Notebook with Anaconda to isolate environments.
  6. Master Statistics: Since statistics and mathematics are the cores of machine learning, begin with descriptive statistics, and gradually move ahead to master inferential statistics. Most of the time, beginners obtain an overall idea of inferential statistics and rely on libraries to carry out statistical analysis. Although this can help complete the task at hand, a weak foundation can limit your ability to think and explore data.
  7. Grasp Mathematics: Mastering mathematics is vital for a data scientist as one can come up with their own methodologies instead of depending on existing libraries. However, to begin, you should know about logarithm, exponential, linear algebra, and more. As you progress, learn calculus and other optimization techniques.
  8. Attend Meetups/Conferences: Engaging with like-minded people can keep you motivated during your learning curve and improve your storytelling skills. Meetups offer a completely different learning experience than the regular online videos, as you can get real-time suggestions or help for your specific challenges from others. Besides, you can also get inspiration by following top data scientists trying to solve strenuous problems with data science.
  9. Master Python Libraries: After getting familiar with Python’s fundamentals, learn the most common libraries like Pandas, Numpy, Matplotlib, Seaborn, Skit-learn, and more. You would require these libraries for almost every project you will work on. Mastering these libraries will save you a few Google searches to speed up your tasks. 
  10. Learn Exploratory Data Analysis (EDA): Exploratory data analysis is the first step in data analytics, where data is assessed to discover patterns, spot outliers, evaluate the spread, and more. A proper exploratory data analysis can help structure the entire process of the project. It will also play a crucial role in assessing your Python and SQL skills; you can choose from Titanic, Netflix movie recommendations, and house price prediction datasets to practice exploratory data analysis.
  11. Data Visualization & Storytelling: Being proficient in data visualization helps understand the data and allows practitioners to tell compelling stories. Since visualization summarises the entire data to communicate immediately, learning to plot can provide an edge over others.
  12. Supervised Machine Learning: As you advance in your data science career path, supervised machine learning is where your machine learning journey begins. Start with simple methods like classification and regression. You will also come across various terminologies like overfitting, underfitting, bias-variance tradeoff, and more. Other standard techniques include linear regression, logistic regression, ridge regression, lasso regression, decision tree, KNN, and Naive Bayes.
  13. Advanced Supervised Algorithms: After supervised machine learning, you can focus on advanced algorithms like the random forest, XGBoost, Catboost, GBM, SVM, and others. These techniques in several use cases help in further optimizing your algorithm to get superior results.
  14. Unsupervised Algorithms: Unlike supervised learning, there are no corresponding values for the input you provide. Unsupervised learning includes clustering and association to unearth patterns that, in most cases, the human cannot. Some of the popular algorithms are K-Means, Hierarchical clustering, DBSCAN, PCA, LDA, and more.
  15. Advance Hyperparameters Tuning Methods And Model Performance: While the above algorithms can help you obtain fairly optimal results, effective hyperparameter tuning can be the game-changer for your machine learning models. Learn techniques like Grid search, random search, Bayesian, and understand different model performance metrics for classification and regression.
  16. Recommendation Engines & TimeSeries Forecasting: Personalizations has become the differentiating factor for many organizations to capture the market. As a result, expertise in recommendation engines becomes crucial for you to learn. Besides, time-series forecasting is another commonly used technique to understand the occurrence of events and predict outcomes. Consequently, you should know SVD and work on recommendations engine projects.
  17. Participate In Competitions: The best way to remember most of what you learned is by practicing in Hackathons and Kaggle competitions. Besides, you can start teaching others by writing blogs and creating Youtube videos. Creating content and participating in contests puts your focus on acquiring in-depth knowledge about several machine learning topics. At this stage, you can also apply for internships to learn while working on real-world projects at data-driven companies.
  18. Neural Networks: Neural Networks can be a very vast concept depending on the use cases. However, you can learn techniques like Artificial Neural Networks and master frameworks like TensorFlow or PyTorch.
  19. Basics Of NLP: As per various studies, 70 to 80 percent of data in organizations are unstructured. This makes natural language processing a crucial technique to bring value from unstructured data. Essential methods involved in this are tokenization, stemming, and lemmatization.
  20. Basics Of Computer Vision: Computer Vision has gained traction due to the numerous use cases. But bias in computer vision is limiting the adoption of the technology. This, in contrast, opens up the opportunity to blaze the trail and develop reliable computer vision-based products. Some of the crucial techniques to learn are CNN and transfer learning.
  21. Apply For Jobs: Eventually, you can apply for data science jobs to work with experts and advance your data science career path in organizations. Jobs can sometimes be correlated to your visibility in the industry. Therefore, you should increase your visibility by publishing blogs, networking in conferences, and being active on LinkedIn.

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Arihant Jain
Arihant is a mechanical engineer by degree and a data scientist by choice. He has over six years of data science experience while working with prominent organizations like ZestMoney, Vodafone, RBL Bank, and Genpact. He has industry experience in data science, machine learning, & AI across the telecom, retail digital, manufacturing, IoT, and banking domain. Arihant is passionate about solving business problems through data science, machine learning, AI. He believes every number has a story to tell. And being a data scientist, it’s his job and passion to decode that story!

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