What are the Skills required to excel as a Data Scientist?

As the demand for data scientists is growing, the field has become more appealing to students and working people alike. When it comes to embracing innovative technology, India is obviously on the front edge. Almost every industry is recruiting data science professionals in India to assist them in gaining insights from large data.

 

Data science is a broad concept that covers data analytics, data mining, AI, machine learning, Deep Learning, and several other similar fields. Data Science is undeniably one of the fastest-growing disciplines in terms of both employment prospects and salary.
 

First of all, What is Data Science?

 

In simple words, it can be said that it is a part of computer science in which we analyze data, inspect its trends and gather meaningful information from it. Data scientists are individuals that use a variety of talents to analyze data acquired from the web, cellphones, consumers, sensors, and other sources in order to extract actionable insights. Data science refers to the process of cleaning, aggregating, and altering data in order to undertake sophisticated data analysis.

 

Data scientists can contribute to almost any organization. If you're a budding data scientist or thinking about becoming one, you already know that the first step is education. Outside of the technical curriculum, however, data science abilities will be transferable across disciplines.

 

 

Non Technical Skills of a Data Scientist

 

Let us first see, what are the non-technical skills you need if you want to pursue Data Science or become a Data Scientist -

 

• Effective Communication

 

Connecting with others is a valuable skill that can help you get things done quickly and effortlessly, whether you're an entry-level employee or the company’s CEO. As we know Data cannot communicate on its own until it is manipulated, which necessitates excellent communication abilities in a Data Scientist. 

 

Another ability that is in high demand almost everywhere is effective communication. In the corporate world, data scientists must be skilled in analyzing data and then communicating their results to both technical and non-technical audiences.

 

See, if you are a data scientist what matters most isn’t that you make another technical guy like yourself understand your finding from a set of data, rather you need to communicate with the non-technical people in the business for which you are working and suggest to them what changes they can make for their improvement. 

 

Let’s be frank, not always your CEO is going to be a technical guy who will understand all those complex findings you have. This critical component fosters data literacy across an organization and increases the ability of data scientists to make a difference.

 

• Being Curious and Analytical Thinking

 

Critical thinking is a useful talent that may be used in any field. It's much more crucial for data scientists since, in addition to uncovering insights, you need to be able to frame questions correctly and comprehend how the results connect to the company or generate actionable next steps. 

 

A genuine interest to solve issues and create answers — especially those that demand some unconventional thinking — is at the heart of the data science career. Data doesn't mean much on its own, therefore a great Data Scientist is driven by a desire to learn more about what the data is saying to them and how that knowledge may be used on a larger scale.

 

( Also Read - Data Science Project Ideas )

 

 

Technical Skills of a Data Scientist

 

There are many skills that a Data Scientist has. We are going to look at some of the most important technical skills, a Data Scientist must possess -

 

• Statistics

 

At a bare minimum, data analysis necessitates descriptive statistics and probability theory. These ideas will assist you in making better decisions for your business based on data. Data Science is the process of extracting knowledge, insights, and making educated decisions from data utilizing various methods, algorithms, or systems. 

 

Making conclusions, estimating, and estimating are all key aspects of Data Science in this situation. Statistics aids in identifying underlying connections or connections that may exist between two variables, as well as forecasting future trends or drifts based on existing data patterns.

 

( Related - Statistics in Data Science )

 

 

• Machine Learning

 

Machine learning is a must-have ability for any data scientist. Predictive models are created using machine learning. For instance, If you want to estimate how many orders your business will be receiving in the following month based on the previous month's data, you'll need to utilize machine learning techniques. 

 

K-nearest neighbors, Random Forests, Naive Bayes, and Regression Models are examples of algorithms used in Machine Learning for Data Science. Machine Learning for Data Science also makes use of PyTorch, TensorFlow, and Keras.

 

• Programming

 

Programming is the only way we can communicate with our computer neighbors in Data Science, you don’t need to be at your best in programming, but yes you need to be comfortable with it. Programming abilities are demanded of a Data Scientist. 

 

Most employers will want you to be proficient in Python, R, and other programming languages. This category includes object-oriented programming, fundamental syntax and functions, flow control statements, modules, and documentation.

 

•  Data Visualization

 

It's a graphical depiction of the information gleaned from the data. Visualizations successfully communicate and guide the investigation to a successful end. Data Visualization plays one of the most important parts in Data Science. 

 

Visualizations communicate well and guide the inquiry. It has always been critical to display information in a way that is both intelligible and pleasing to the eye. One of the skills that Data Scientists must learn in order to connect more effectively with end-users is data visualization computer neighbors. 

 

You should be comfortable with basic plots such as histograms, bar charts, and pie charts before moving on to more sophisticated charts such as waterfall charts and thermometer charts. The tools utilized in this stage are primarily Tableau and Power BI, both of which have a user-friendly interface.

 

 

• Data Manipulation and Analysis

 

The quality of data is only as good as the individuals who are analyzing and modeling it. A Data Scientist is expected to be highly proficient in this field. Data manipulation is the process of cleaning and transforming data into a format that may be properly examined in subsequent phases. 

 

A Data Scientist should be able to examine data, perform tests, and develop models to acquire fresh perspectives and anticipate probable outcomes based on a foundation of both logical thinking and communication. This is generally the point at which you get a lot of knowledge about the data. For example, what are the typical monthly sales, which goods generate the most orders, and so on? 

 

Data analysis is generally performed in Excel, SQL, or Python with Pandas, and it is the most essential work of an analytics expert, whereas data analysis is a stage in the machine learning process.

 

( Also Read - Skills Required by an AI/ML expert )

 

 

•  Microsoft Excel

 

MS Excel aids data science by allowing users to name and create ranges, as well as filtering, sorting, merging, cleaning, and trimming data. Pivot tables and charts may be created, and Visual Basic for Applications can be used (VBA).

 

Conclusion

 

Data Scientist is a very flourishing career and is growing day by day. Large IT businesses are no longer the only ones in need of data scientists as more data becomes more accessible. 

 

A scarcity of competent individuals available to fill open positions is posing a challenge to the rising need for data science experts across businesses, large and small. The need for data scientists is not expected to decrease in the future years. So it’s better than if you are interested in this field, you should start developing these skills right away in order to achieve your career goals.