Learn How to Work on Data Analysis Projects with Python via this Coursera Program

Nikhil Agrawal

28 December 2022

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Course Overview

Data Analysis with Python course teaches you how to perform data analysis using Python. This course will teach you the basics of Python and help you explore many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more.

Technically, it will commence by importing datasets, followed by data cleaning and manipulation. Upon manipulation, the data will be summarized and the search for the robust model deployment would begin which probably will end with pipelining and ensembling. The course provides a theory-cum-practical learning experience with data analysis projects in python for beginners.

You would be exposed to significant relevant modules as well as libraries which are usually employed for data analysis like Pandas, Numpy, and Scipy. You will be introduced to Scikit-learn, an open-source library and provided a demonstration of the same in terms of cool predictions upon testing smart models. If you choose to take this course on data analysis projects for beginners and earn the Coursera course certificate, you will also earn an IBM digital badge.

"This course helped me as it provides a theory-cum-practical learning experience for the learners."

- Nikhil Agrawal

Course Structure

It is a self-paced, well-curated beginner-level course spread over 14 hours, taught online by experienced faculty members to equip the demarcated target audience attending the course. The course provides a theory-cum-practical learning experience for the learners.

Joseph, one of the instructors of this course, has a doctorate in Electrical Engineering. His research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his doctorate. Some crucial potencies of the course include Predictive Modeling, Python Programming, Data Analysis, Data Visualization, data analysis projects for students etc.

Technically, the course is spread over 6 distinct modules:

Module 1: Importing Datasets

Module 2: Data Wrangling

Module 3: Exploratory Data Analysis

Module 4: Model Development

Module 5: Model Evaluation

Module 6: Final Assignment

Insider Tips

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

Placement or Internship Assistance

Individuals in need of placement assistance are provided with sufficient opportunities upon course completion. The course equips you to be market-ready, and they certainly assist you in curating your resume. You would be directed to relevant job websites and job drives, both online and offline, along with sufficient networking sessions so that you can grab a decent offer.

Technical Exhibitionism from the Course

The course provides theory-cum-practical learning experience with real time data analysis projects for the learners. You would be exposed to significant relevant modules as well as libraries that are usually employed for data analysis like Pandas, Numpy and Scipy. You will be introduced to Scikit-learn, an open-source library and provided a demonstration of the same in terms of cool predictions upon testing smart models.

Be Proactive

During the course, I invested a decent amount of time discussing technical aspects attained via the course. My learning cohorts were quite interactive throughout, which genuinely made the learning experience fun, and worth remembering.

Assessment and Grading

There would be a qualified assessment evaluation criteria at the end where the learners need to appear for the evaluation, followed by feedback submission. After every week, MCQs and assignments test learners' knowledge. Upon failing, stringent action would be taken, one of the consequences might be your grades being marked low despite thorough interpretation of concepts. So, one should be watchful regarding the deadlines provided.

Final Take

Being a data analyst, I need to be well versed in the Python programming paradigm as I need to exploit the criticalities of this paradigm in my day-to-day work. Considering that it is a very niche field, this course helped me as it provides a theory-cum-practical learning experience with different data analysis projects with python.

It also exposes learners to significant relevant modules and libraries usually employed for data analysis like Pandas, Numpy and Scipy. Also, you will be introduced to Scikit-learn, an open-source library and provided a demonstration of the same in terms of cool predictions upon testing smart models.

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