The name ‘data science’ itself can be overwhelming for many but can prove to be a perfect career path for those who’ve got a flair for numbers and a way with computers. While it helps to have a working knowledge on the domains of statistics, programming, distributed computing, databases, machine learning, experimental design, deep learning, and natural language process – it is also important to have the ability of complex problem-solving. In 2018, there are as many as 2.8 million data science professionals across the world. According to IBM, even this figure is predicted to show a spike of 28% by the year 2020. Rated by Harvard Business Review as one of the most demanded job titles of the 21st century, there’s no dearth of data science courses for those who wish to learn.

What is Data Science?
Before embarking on the guide to becoming a successful data scientist, we need to understand what is data science. IBM defines data science as the combination of machine learning models with advanced prescriptive modeling to derive solutions and forecasts about business decisions. In simpler words, its the process of asking challenging questions, and answering the same by using data. In general terms, a typical data science workflow resembles the following:

Identify the problem.

Gather data that will give meaningful insights into the problem.

Manipulate the data by exploration and analyses.

Build and evaluate a machine learning model.

Communicate results.
While this workflow looks seemingly simple, it does require a thorough understanding of a data manipulation programming language, and expertise in working with large volumes of data. So, keeping the needs of the industry in mind, the following is a descriptive guide to give the necessary kickstart to your data science career:

Numerical and technical skills:
Since data scientists primarily deal with data, it is crucial for them to understand the techniques of data analysis that include statistical/predictive analysis, data cleaning, and preparation, interpretation of statistical output (including ANOVA, regression testing, hypothesis testing). It would also help to have a basic idea of time series, clustering, forming decision trees, Bayes and more. While developing machine learning models is an altogether separate topic of study, certain machine learning principles like random forests, boosting, support vector machines, bagging, recommender systems, and neural networks are also critical areas of research. While these skills are descriptive on their own, it is more important for data scientists to know which technique would be the best fit to solve a given problem.

Gain proficiency in a programming language:
When it comes to starting a career in data science, Python and R are both fantastic first choices as a programming language. While R’s popularity is more prevalent in the academia, Python is the preferred choice of recruiters when it comes to working in the IT industry. Further, when compared to R, Python is more user-friendly and hence, is the best entry route for beginners. Irrespective of the language you opt to begin with, it is essential to get acquainted with the entire ecosystem of data science packages. Beginners also need to master topics related to data structures, functions, imports, conditional statements, loops, comparisons, and comprehensions.

Learn about data manipulation, analysis, and visualization:
Once you’ve mastered how to work with a data manipulation programming language, the next step is to learn how to handle data. For those working with Python, it is essential to determine the use of the pandas library. The pandas library provides Python coders with DataFrame, which is a high-performance data structure similar to an Excel spreadsheet or SQL table. The DataFrame comes in use while handling tabular data which is associated with different types of columns. It also includes tools for managing missing data, reading and writing data, merging datasets, cleansing messy data, and visualising data, among others. Therefore, a thorough knowledge of the pandas library will significantly increase a data scientist’s ability to work with data.

Get familiar with machine learning:
One of the most demanding roles of a data scientist is building machine learning models to automatically extract insights from data, and to predict the future. When it comes to machine learning with Python, scikit-learn is the most popular library, and for all the right reasons. Not only does it provide a consistent and clean interface for a variety of different models, but it also offers tuning parameters for individual models while choosing intelligent default variables. Further, its excellent documentation makes it easy for data scientists to understand its models for later use in their predictive modelling techniques.
However, before you deliver your predictive model, always make sure that it answers the following questions:

Will my machine learning model work best with my dataset?
How do I interpret my model’s results?
How can I evaluate whether my model will provide solutions to future problems?
If you’re confident about the answers to the above questions, then your machine learning model is ready for deployment.

The world is increasingly going digital, and so are the operations and processes in every business sector. This translates to only one conclusion, that data science is here to stay. As a result, the growing demand for big data and data analytics makes it a fantastic career move for those who are looking at becoming data scientists. With the right qualifications, one can enjoy a bright career in this industry. As companies realise the need for specialists in data analytics and sciences, the career outlook for data science is only going to improve.