Want to Become a Data Scientist? This edX Program is Just for You!

This Introduction to Data Science is an online, self-paced certification IBM course brought to you by edX. It is a program that will help you gain insights into the world of data scientists. You will not only learn about data science and what data scientists do, but get hands-on training on tools and algorithms used daily within the field of data science. It will also reflect on Data visualization and data analytics using Python. This course will help you gain top data analyst skills. Embark on a transformative journey to become a data scientist, unlocking endless possibilities in the world of data-driven insights and innovation and 

The instructors provided a lot of insight into what it means to be a data scientist, from working with insufficient data to presenting your work to C-class management. I highly recommend this data science course for beginners and intermediate-level data analysts. The instructors for the course include Professor Murtaza Haider and Professor Norman White.

Here are a few steps to becoming a data scientist, you can follow a structured path that includes gaining a strong foundation in mathematics and statistics, learning programming languages such as Python and R, acquiring knowledge in data manipulation and analysis, developing skills in machine learning and data visualization, and gaining practical experience through real-world projects and internships. It takes around 6 weeks to complete the course, with learners required to put in 3–6 hours of work a week. The course structure is divided into 1 exercise and 6 modules, each covering a wide variety of data science topics.

• The course begins by defining data science in Module 1, explaining key fundamentals in the field of data science and why data scientists are in high demand.
   
• Module 2 is about what data scientists do, how organizations use data science to solve problems, some key concepts, tools and algorithms shared by data science professionals, and the role of the cloud in data science.
   
• Module 3 was a little challenging, and things got much more complex and tedious; lot of topics were new to me. In this, I learnt about big data and mining, the 5 Vs. of Big Data, how Hadoop and other tools are handling the demands of big data, data mining and the steps that comprise the process of mining a given data set.
   
• Module 4 explains the difference between Machine Learning (ML) and Deep Learning (DL) and their applications. Other topics covered are: What is Regression? and how to use Regression to analyze data. Then there is 1 exercise to facilitate hands-on skills development and enable us to complete some labs in this course with a special feature code for IBM Cloud.
   
• In Module 5, you will learn how businesses like Netflix, Amazon, UPs, Google, and Apple use the data generated by their consumers and employees; ways that consumers generate data then companies capture that data and analyze it. 
   
• Module 6 is about what a career in data science entails and the process of recruitment. It covers the structure of the final report for a data science project which is, a cover page, table of contents, and executive summary. In short, it must soundly communicate data reports.

On completion of the week's syllabus, assessments are given. Study materials provided throughout the week are enough to attempt these assessments.

Data is meaningless if it cannot be processed to gain insights. The field of data science gives you the tools and methods to process data sets effectively and get the most from the data you collect.

I am currently a student at Shri Ramdeobaba College of Engineering Nagpur. I have been selected for Infocepts through the ‘Skill Based Training-Cum-Hiring Program’ for the 2023 batch. Doing this course has helped me in implementing practical empowerment programs, strategies, and policies.

I aim to become a data scientist as I believe it is the best time to be one. Today, when data is generated from multiple sources, data science provides amazing insights into business and enables a more sustainable and proficient model of business. Through this course, I was able to figure out what a data scientist role demands. I got to know what companies want from a data scientist and the different methodologies used by a data scientist.

I was also able to understand the 2 stages of data science methodology, which are business understanding and analytic approach. Becoming a data scientist requires acquiring a solid foundation in mathematics, statistics, and programming, along with developing expertise in data manipulation, analysis, machine learning, and data visualization, and gaining practical experience through hands-on projects and continuous learning to stay abreast of emerging trends and technologies.

This course is meant for beginners in data science. Even in the coming years, it will stand the tides of time.