DATA ANALYST COURSE

Discover Insights from Data with Python and SQL

COURSE BUILT IN COLLABORATION WITH 
KAGGLE

Before You Start Prerequisites:

In order to succeed in this program, we recommend having experience working with data in Python (Numpy and Pandas) and SQL.

Program Info

This program prepares you for a career as a Data Analyst by helping you learn to organize data, uncover patterns and insights, draw meaningful conclusions, and clearly communicate critical findings.


You’ll develop proficiency in Python and its data analysis libraries (Numpy, Pandas, Matplotlib) and SQL as you build a portfolio of projects to showcase in your job search. 


Depending on how quickly you work through the material, the amount of time required is variable. We have included an hourly estimation for each section of the program.


The program covers one term of three month (approx. 13 weeks). If you spend about 10 hours per week working through the program, you should finish the term within 13 weeks.


Students will have an additional four weeks beyond the end of the term to complete all projects.

  • Length of Program: 1 term, 13 weeks and approximately 200 hours
  • Frequency of Classes: Self-paced within the 13-week term
  • Textbooks required: None
  • Instructional Tools Available: Video lectures, Text instructions, Quizzes, In-classroom mentorship

This is a self-paced program and the length is an estimation of total hours the average student may take to complete all required coursework, including lecture and project time. 

Actual hours may vary.


Intro Project

Explore Weather Trends (5 hrs) 

This project will introduce you to the SQL and how to download data from a database. You’ll analyze local and global temperature data and compare the temperature trends where you live to overall global temperature trends.

1

Project 1

Investigate a Dataset (40 hrs)

In this project, you’ll choose one of curated datasets and investigate it using NumPy and Pandas.

You’ll complete the entire data analysis process, starting by posing a question and finishing by sharing your findings.

2

Project 2

Analyze Experiment Results (45 hrs)

In this project, you will be provided a dataset reflecting data collected from an experiment. You’ll use statistical techniques to answer questions about the data and report your conclusions and recommendations in a report.

3

Project 3

Wrangle and Analyze Data (50 hrs)

Real-world data rarely comes clean. Using Python, you'll gather data from a variety of sources, assess its quality and tidiness, then clean it.

You'll document your wrangling efforts in a Jupyter Notebook, plus showcase them through analyses and visualizations using Python and SQL.

4

Project 4

Communicate Data Findings (50 hrs)

In this project, you will use Python’s data visualization tools to systematically explore a selected dataset for its properties and relationships between variables.

Then, you will create a presentation that communicates your findings to others.