What Is Data Science, and What Does a Data Scientist Do

1. SYNERGISTICIT https://www.synergisticit.com/data-science

2. What Is Data Science, and What Does a Data Scientist Do? Data is undoubtedly driving today's world. Every activity you perform online creates some data, whether you send an email, watch a movie, browse through your social media account or make some transaction. Can you guess how much data is produced every day? 1.145 trillion MB/day. And, it will surpass this number very quickly, considering the growing number of internet users every passing day. A source predicts that by 2025, 463 exabytes of data will be created. Isn't it crazy? It does sound so. Is this data useful? Yes, very much. There are many insights that data can offer to the stakeholders and help them take concrete business decisions. This is where comes data science.

3. What Is Data Science? You must have heard this term pretty often these days. In fact, it is already optimizing your search results, influencing your recommendations and buzz feed, and so much more. In fact, data science is pervasive in industries grappling with loads of data and looking for ways to make sense of this enormous pile of disparate data. It is a study of data that combines many fields to gain extract the information present in the data.

4. Who Is A Data Scientist? Data scientists are experts in data who know how to find the meanings concealed in messy data gathered from a number of sources. They perform various functions on data to collect, organize, clean, analyze it and finally visualize and communicate the insights. With the help of their reports, they help the business create actionable plans for the future. They are knowledgeable in various fields from software engineering to programming, mathematics, and statistics and work at the forefront for solving business problems.

5. What Does A Data Scientist Do? Data scientists can work in any industry, and depending upon the industry type, their responsibilities can be modified a bit, but in general, the duties are the same. So, here's what a data scientist has to do.

6. Writes data algorithms and modifies the existing ones to help maximize results and get solutions specific to the business problem. • Go through relational and non-relational databases and integrate the structured and the unstructured data to the data lakes, warehouses, etc. • Find out and transform the most optimal features/variables from the raw data for predictive modeling, improving the accuracy of the model on unseen data. • Understand all the statistical and programming tools, libraries, and packages to choose from for specific data science applications.

7. Ensure the integrity of the data to get precise and reliable results and save time, money, and efforts wasted because of inaccurate information. • Set up quality standards and fix data quality to tackle garbage in, garbage out (GIGO). • Implement the best tools, framework, and algorithms for getting accurate results • Figure out ways to leverage data for solving complex business problems and achieving business goals. • Work collaboratively and communicate with other data team members such as data scientists, data engineers, business analysts, etc.

8. Mitigate the risks caused by the decisions made on erroneous data and statistics. Understand the industry, market, products, and services to help in the betterment of the business

9. Organizations across industries are looking forward to the data scientist who, with their expertise, can get them an accurate picture of what's happening to their businesses and how they can solve the underlying problems in their strategies and processes, taking a data-centric approach. Data science jobs will grow by 28% through 2026 as estimated by the U.S. Bureau of Labor Statistics. The quickest way to get into this lucrative profession is through a data science coding bootcamp. It will help you acquire critical data science skills in a couple of months and qualify for numerous data science roles, including data scientist, data analyst, data engineer, machine learning scientist, business analyst, and more.

10. Essential Skills For Data Scientists There are many core skills that a data scientist must possess to accomplish the tasks. Look at them below. • Statistical concepts: You should know about statistical tools and formulas to understand and identify the hidden patterns in data. Regression hypothesis testing, descriptive statistics, probability theory are some of the key concepts. • Mathematics: You should be familiar with mathematical concepts, such as arithmetic, linear algebra, calculus, geometry, probability, equations and graph, Bayes theorem as they help implement algorithms and perform analysis. • Machine learning: Knowledge of ML help simplify the data science tasks by automating them and improving productivity. ML algorithms help the machines learn on their own.

11. Computer science: Computer science knowledge helps understand Big data, especially when working with concepts like map-reduce, master-slave, etc. • Programming: Knowledge of programming is a must for data science, and you should have familiarity with languages like R, Python, and SQL. It's useful in everything from analyzing large datasets to automating data organization, cleaning, and designing databases. • Visualization and storytelling: Data scientist has to communicate the results to the stakeholders so that they can take further actions based on the data. For this, you need to have exceptional visualization skills, putting the data in the form of graphs, tables, and other easy-to-understand representations and then narrating the insights drawn from it.

12. As you can clearly see, a data scientist is a multi-talented individual who has to have more than one skill. But this is not it. Besides the technical skills, you must have soft skills too to succeed in your field.

13. Business instinct: As data scientists work closely with the stakeholders and help solve business issues. So, you need to have some business sense to help people get answers to the most pertinent questions. • Analytical thinking: You will be able to offer unique solutions to problems only when you identify them. Analytical thinking will help you identify the root cause of the problem and develop solutions that work the best. • Critical thinking: Many business decisions require complex analysis and data interpretation to offer a sensible and unbiased conclusion to problems. Critical thinks help data scientists cope with issues as they come. • Curiosity: Curiosity is necessary to go deeper and look beyond what's visible and known to discover hidden information within the data. • Interpersonal skills: You should be able to put across your findings to the team and make them accessible. Your communication skills should be brilliant.