Data Analyst

About Course

Clean up messy data, uncover patterns and insights, and communicate your findings. You’ll start with an introduction to data analysis tools, including Jupyter Notebook, NumPy, pandas, and Matplotlib. Using these tools, you will ask questions about data and answer them through data collection, exploration, wrangling, and visualization. This intermediate-level program includes real-world projects where you will choose your own datasets, research questions, and analysis approach. As you progress through the program, each course will repeat the data analysis process while introducing more advanced techniques, such as applying data imputation to fill in missing data and applying appropriate encodings when developing data visualizations.

A meticulously crafted data analyst online course that imparts essential skills for cleaning up messy data, uncovering patterns and insights, making predictions with machine learning, and effectively communicating findings. This intermediate-level program involves real-world projects where learners can apply their skills in data visualization, exploratory data analysis, latent variables, and more. The curriculum includes hands-on experience with Python, Pandas, NumPy, as well as advanced data wrangling and visualization using Matplotlib and Seaborn. We empower our learners with practical, industry-relevant skills. Our data analyst course is designed not just to impart knowledge but to ensure its application in real-world scenarios, enhancing both understanding and skill retention. Join us to advance your career in data analysis, where we provide the tools and support for your professional growth.

Course Content

An Introduction to the Data Analyst Program
Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

The Data Analysis Process
Learn about the data analysis process and the Python packages used in this course

Jupyter Notebooks
Jupyter Notebooks are a great tool for sharing insights and visualizations alongside your code. This lesson covers how to create them and utilize their various features.

Exploring and Inspecting Data
Use the pandas library to load data, view its properties, and start asking data analysis questions

Manipulating Data using Pandas and NumPy
Use the pandas library to perform data cleaning, filtering, and reshaping tasks. This includes troubleshooting issues with data as well as optimizing for memory usage and speed.

Communicating Results
Draw conclusions and communicate results to stakeholders by calculating statistics and creating basic data visualizations with the pandas library

Investigate a Dataset
Perform an investigation, and share your findings.

Introduction to Data Wrangling
You will learn what data wrangling is and why it matters. And you will see a real-world example of data wrangling and some common misconceptions about data wrangling.

Gathering Data
You will learn to implement data gathering methods to obtain and extract data from various sources and in several popular data formats.

Assessing Data
You will learn to identify different data quality and structural issues and apply visual and programmatic assessments to catch them.

Cleaning Data
You will learn to remediate the issues you identified in the assessment stage and test that your data cleaning is successful.

Real World Data Wrangling with Python
You will apply the skills you acquired in the course by gathering, assessing, and cleaning multiple real-world datasets of your choice.

Data Visualization in Data Analysis
In this lesson, see the motivations for why data visualization is an important part of the data analysis process and where it fits in.

Design of Visualizations
Learn about elements of visualization design, especially to avoid those elements that can cause a visualization to fail.

Univariate Exploration of Data
In this lesson, you will see how you can use matplotlib and seaborn to produce informative visualizations of single variables.

Bivariate Exploration of Data
In this lesson, build up from your understanding of individual variables and learn how to use matplotlib and seaborn to look at relationships between two variables.

Multivariate Exploration of Data
In this lesson, see how you can use matplotlib and seaborn to visualize relationships and interactions between three or more variables.

Explanatory Visualizations
Previous lessons covered how you could use visualizations to learn about your data. In this lesson, see how to polish up those plots to convey your findings to others!

Communicate Data Findings
Perform an exploratory data analysis using Python. Then, create a presentation with explanatory plots that conveys your findings.

KeyStone Project

About Course

What Will You Learn?

Course Content

An Introduction to the Data Analyst Program Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

The Data Analysis Process Learn about the data analysis process and the Python packages used in this course

Jupyter Notebooks Jupyter Notebooks are a great tool for sharing insights and visualizations alongside your code. This lesson covers how to create them and utilize their various features.

Exploring and Inspecting Data Use the pandas library to load data, view its properties, and start asking data analysis questions

Manipulating Data using Pandas and NumPy Use the pandas library to perform data cleaning, filtering, and reshaping tasks. This includes troubleshooting issues with data as well as optimizing for memory usage and speed.

Communicating Results Draw conclusions and communicate results to stakeholders by calculating statistics and creating basic data visualizations with the pandas library

Investigate a Dataset Perform an investigation, and share your findings.

Introduction to Data Wrangling You will learn what data wrangling is and why it matters. And you will see a real-world example of data wrangling and some common misconceptions about data wrangling.

Gathering Data You will learn to implement data gathering methods to obtain and extract data from various sources and in several popular data formats.

Assessing Data You will learn to identify different data quality and structural issues and apply visual and programmatic assessments to catch them.

Cleaning Data You will learn to remediate the issues you identified in the assessment stage and test that your data cleaning is successful.

Real World Data Wrangling with Python You will apply the skills you acquired in the course by gathering, assessing, and cleaning multiple real-world datasets of your choice.

Data Visualization in Data Analysis In this lesson, see the motivations for why data visualization is an important part of the data analysis process and where it fits in.

Design of Visualizations Learn about elements of visualization design, especially to avoid those elements that can cause a visualization to fail.

Univariate Exploration of Data In this lesson, you will see how you can use matplotlib and seaborn to produce informative visualizations of single variables.

Bivariate Exploration of Data In this lesson, build up from your understanding of individual variables and learn how to use matplotlib and seaborn to look at relationships between two variables.

Multivariate Exploration of Data In this lesson, see how you can use matplotlib and seaborn to visualize relationships and interactions between three or more variables.

Explanatory Visualizations Previous lessons covered how you could use visualizations to learn about your data. In this lesson, see how to polish up those plots to convey your findings to others!

Communicate Data Findings Perform an exploratory data analysis using Python. Then, create a presentation with explanatory plots that conveys your findings.