Start a Career in Data Analysis with this Udacity Program

Piyush Kumar

28 December 2022

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Course Overview

Become a Data Analyst course teaches you to employ Python, SQL, or Statistics to discover insights, communicate important findings, and develop data-driven solutions. You will improve your programming skills and your ability to deal with complex, messy data. This course will teach you how to prepare and manipulate data for analysis and create visualizations that can be used for data exploration. You'll also learn how to use your data skills in order to tell stories with data.

The curriculum is very well-designed and has a university-style structure with lectures, quizzes, case studies, and graded projects. You can learn at your own pace, interact with fellow learners, and have your doubts answered (almost instantly) by the mentors. They also assist in profile reviewing services for your resume and provide a 1:1 session with the career coach for career development which helps understand how to become data analyst successfully, requirements for a career in data analysis, career options in data analysis, etc.

Learners start with basic concepts and later move to a more advanced level of detailed explanation of the subject matter. Timely assessments are provided to keep your learning up-to-date along with supporting materials in the form of external links, pdfs, presentations, notable papers etc. Interactive hands-on graded projects are also an integral part of the course as they allow working on real-world problems/scenarios to improve technical knowledge and data analyst skills.

"I use the course lessons about SQL, Database Management, Data Modeling, etc. almost on a daily basis in my work."

- Piyush Kumar

Course Structure

This online certification program is an intermediate-level course spread over 4 months. It is self-paced which makes it easier for working professionals to enroll. It requires an effort of at least 10 hours per week.

The professors associated with this course have degrees from renowned institutions in the field of cloud development. This is a great opportunity to study from an international faculty. The applicants get to be a part of a Capstone project that enhances their conceptual learning. Such projects foster an innovative study environment for the learners.

The course offers instructor moderated discussions that are great opportunities to make and build connections. The learners enjoy the benefits of hands-on training in the course. The curriculum prepares the learners for careers in data analysis. I wanted to pivot to the Analytics domain but as a precursor and refresher, I preferred to do an open-source course like this which has a well-curated curriculum.

Technically, the course is spread over 4 prime modules formulated as:

Module 1: Introduction to Data Analysis
Module 2: Practical Statistics
Module 3: Data Wrangling
Module 4: Data Visualization with Python

Insider Tips

To get the best out of this course, I have included some important tips below that I think you might find useful:

Assignments or Grading Criteria

The assessment method involves completing assignments as well as undertaking projects under various modules, throughout the duration of the degree. Quizzes and human-graded projects can be found all throughout the course and can be attempted multiple times. The final assessment ends with a Capstone project.

Hands-on Training

The modules of this course are detail-oriented, comprehensive and practical. Besides theoretical concepts, the learners also learn through hands-on training. This helps learners advance their programming skills and refine their ability to work with messy and complex datasets. The course offers learning through solving real life cases. This provides extensive exposure to the candidates.

Career Assistance

The course also offers assistance and guidance related to jobs. The mentors will guide the process of learning and resolve all your queries. The course contains a Capstone project that helps you enhance your knowledge. The course offers personalized feedback to all the learners so that they can improve upon them.

Prerequisites or Requirements

Some prerequisites that I would recommend for this curriculum are:

Have experience working with Python (specifically NumPy and Pandas) and SQL
Familiarity with Python standard libraries
Working on data with Pandas and NumPy

Final Take

I am currently working as a Data analyst. I have learned a lot about Data Science, and feel a lot more confident about working with Python, Pandas, Numpy, SQL, and Statistics. I use the course lessons about SQL, Database Management, Data Modeling, etc. almost on a daily basis in my work.

These skills which I picked up during the data analysis courses are referred to as the ‘bread and butter’ for a Data Analyst. In our company, we use SQL, Python, and statistics to generate impactful insights that in turn feed our business decisions. Studying this course has helped me contribute meaningfully to my workplace and has opened different career opportunities in data analysis.

According to my observations, the need for digital data, databases, and the whole infrastructure surrounding that data will be relevant as long as the Internet is here. Data-driven insights can be a game-changer; no one would want to miss out on that. With increasing technological advancements in all walks of life, this will only get bigger and better.

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