Skills to become a good data scientist

Data Science is trending across diverse industries. A study suggests that the market for data science platforms is anticipated to grow from USD 95.3 billion in 2021 to USD 322.9 billion in 2026. Many people have questions on how to become a data scientist? 

Data scientists and other experts in big data are in high demand as more data is generated at an exponential rate. A data scientist must gather and examine huge, organized, and unorganized data sets. This occupation analyzes massive amounts of data using math, statistics, and computer science expertise before using the knowledge to create practical solutions for organizations.

It focuses on investigating the information’s origin, unlocking the patterns therein, and ultimately turning it into a resource for businesses. 

The average data scientist salary in India is INR 8,60,316. Data Scientists with less than one year of experience and freshers earn an average yearly salary of INR 5,71,493. 

In this article, we will examine technical and non-technical data scientist abilities to help you become a good data scientist.

How to become a data scientist?

Many companies are hiring data scientists around the world. TCS, Accenture, and Wipro are some of the best companies to work for as data scientists in India. In general, formal coursework is required to become a data scientist. 

But, how to become a data scientist? Here are a few factors to consider.

• Earn a degree in data science

Employers prefer to see university credentials to guarantee you have the knowledge to handle a data science job, although it is not always essential. To gain a head start in the industry, consider pursuing a relevant bachelor’s degree in data science, analytics, or computer science.

• Improve your related talents

If you feel you can enhance your hard data skills, consider taking an online course or participating in a relevant Bootcamp. Here are some of the skills you must possess.

• Computer languages: Data scientists can anticipate spending time sorting through, analyzing, and managing massive amounts of data using programming languages. Python, R, SQL, and SAS are examples of popular data science programming languages.

• Data visualization: Developing charts and graphs is a crucial part of being a data scientist. You should be familiar with the following tools: Tableau, PowerBI, and Excel.

• Machine learning and deep learning: Using machine learning techniques in your data science job means constantly increasing the quality of the data you collect and maybe being capable of predicting the outcomes of future datasets. A machine learning course can teach you the fundamentals.

• Big data: Some companies may need to see that you possess experience working with big data. Hadoop and Apache Spark are two software technologies used to process big data.

• Communication: Even the most bright data scientists will be unable to make a change if they cannot effectively convey their results. Therefore, the ability to communicate ideas and results vocally and in writing is a highly sought-after skill among data scientists.

• Obtain an entry-level data scientist position

Though there are numerous paths to becoming a data scientist, a similar entry-level position can be a suitable starting point. Look for jobs using a lot of data, such as data analyst, business information analyst, statistician, or data engineer. 

• Prepare for interviews in data science

Because data scientist employment can be extremely technical, you may be asked technical and behavioral questions. Prepare for both by stating your response aloud. Having examples from your previous professional or academic experiences on hand will help you appear confident and competent to interviewers. 

READ MORE: The data science roadmap explained

Key skills for a data scientist

Although career opportunities in data science are expanding, there is a shortage of data scientists with the requisite skills. Below we have discussed the various skills required for data scientists.

Technical skills	Soft skills
Programming Language & Database Mathematics & Statistics Data Manipulation and Analysis Data Visualization Web scraping Machine Learning Deep Learning Big Data Software Engineering Model Deployment	Problem solving skill Communication skills Storytelling skills Strategic thinking Collaboration

Technical skills

Let’s go through some of the technical skills needed to become a data scientist.

1. Mathematics and statistics

Data Science is extracting knowledge and insights and making informed decisions from data using capital methods, algorithms, or systems. Making conclusions, estimating, or predicting are thus fundamental aspects of Data Science. 

Probability, when combined with statistical approaches, helps in the generation of results for future investigation. Statistics is mostly based on probability theory, in addition to probability distributions, sampling and population, CLT, skewness and kurtosis, inferential statistics such as hypothesis testing and confidence intervals, and so on.

2. Data manipulation and analysis

It is critical to know how to cope with data flaws. Data manipulation is changing and mapping raw data from one form to another to prepare the data for further study. You simply obtain data and important aggregate variables and then purify the data for data manipulation.

Understanding analytical tools are important for a data scientist’s ability to get meaningful information from a well-organized data source.

3. Data visualization

Data visualization is a graphical depiction of the results of the data analysis. To begin, you should be familiar with plots such as histograms, bar charts, and pie charts, and then go to more sophisticated charts such as waterfall charts, thermometer charts, and so on. 

These graphs are quite useful during the exploratory data analysis stage. Colorful graphics make univariate and bivariate studies much easier to grasp.

4. Web scraping

Theoretically, any data that exists on the internet may be scrapped as needed. Companies utilize this technology to extract relevant data such as text, photos, videos, and other important information to increase productivity. 

Details might include client feedback, surveys, polls, and so on. Companies of all sizes (from start-ups to large) are actively employing this approach, and using specific tools and software for this method can ease this process 

5. Machine learning

Predictive models are created using machine learning. You may begin with simple linear and logistic regression models and progress to sophisticated ensemble models such as Random Forest, XGBoost, CatBoost, and others. Knowing the code for these algorithms is beneficial.

6. Deep learning

Are you inspired by smart assistants, the amazing self-driving vehicle segments? Deep learning made it all possible. Because of developments in data storage capacities and computing innovation, it is a high-growth sector in the field of Artificial Intelligence.

To flourish in this sector, you must be proficient in programming (ideally Python) and linear algebra, and mathematics. To begin, you may design simple models before progressing to more complicated models like CNN, RNN, and others.

7. Big data

The data is generated at a rate of 2.5 quintillions each day! The advent of the internet, social media networks, and IoT has dramatically increased the amount of data we generate. This data has a high volume, velocity, and veracity, which are the three V’s of Big Data.

Organizations have been inundated with such a big volume of data, and they are attempting to deal with this data by fast embracing big data technology so that this data may be effectively maintained and accessed when needed.

8. Software engineering

To generate high-quality code that will not cause problems during the production stage, it is required to understand the fundamentals of various software engineering topics such as the basic life cycle of computer development projects, data types, compilers, time-space complexity, and so on.

Writing effective and clean code will benefit you in the long term and will allow you to work with your team members. Again, you do not need to be a software engineer, but understanding the fundamentals will be beneficial.

9. Model deployment

Model deployment is the most underappreciated stage in the machine learning process.. This role is often performed by machine learning engineers. However, it differs depending on the company in which you work. Even if it is not a job requirement at your organization, it is critical to understand the fundamentals of model deployment and why it is vital.

Soft skills

Let’s go through some of the soft skills needed to become a data scientist.

1. Problem-solving skill

The capacity to handle complexity is essential for developing a career as a data science expert. When necessary, one must ensure the capacity to discover and generate both unique and effective solutions. You may experience difficulties in developing any solution that requires clarity in data science ideas by breaking down the problems into various sections and aligning them in an organized manner.

2. Communication skills

A data scientist must translate exceedingly complicated concepts into layman’s words and understandable examples. When attending a meeting, this talent might be valuable in persuasion and bargaining. If a non-technical data user understands the gist of what you are saying, you have communicated effectively.

3. Storytelling skills

Data scientists do descriptive, predictive, and actionable insights to study, evaluate, and disseminate data findings. They can provide important context and successfully convey their ideas to all present by using their storytelling talents. A solid storytelling narrative may make data and analytics much more approachable.

4. Strategic thinking

A certain amount of business acumen is essential for a Data Scientist to efficiently use data in a way that is valuable to their organization. You must completely comprehend the company’s core objectives and goals, as well as how they affect the job you accomplish. 

You must also be able to develop solutions that achieve those objectives while being cost-effective, simple to deploy, and widely adopted. Strategic thinking considers challenges from several angles. This is a learned talent, but you can surely improve.   

5. Collaboration

Data scientists must collaborate with company leaders to establish strategies, enhance products, launch improved conversion efforts, and work with server and client software developers to optimize workflow and build data pipelines. There will be various hurdles along the path. Therefore it is critical that you examine other people’s perspectives and thoughts, even if they differ from your own.

Summing it up

Data Science is still evolving, and learning in this discipline never ends. One day you master the tool, and the following day it is run over by a more complex tool. Choosing the right course to get your desired degree is a big decision, yet it can be difficult at times. The online data science course by the Manipal Academy of Higher Education (MAHE) is suitable for anyone who is looking to learn how to establish a profession in data science.
Visit Online Manipal and enroll in a data science course to benefit from outstanding teaching faculty and an efficient course structure.

Key takeaways:

• Data scientists and big data specialists are in great demand as data volumes increase at an exponential rate.
• As a data scientist, your primary responsibility will be to collaborate with your firm to identify problems and utilize data to develop solutions for better decision-making.
• Organizations are increasingly reliant on data scientist skills to survive, expand, and stay one step ahead of their competition.