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Path overview
You’ll begin with Excel, where you’ll learn how to manipulate data using complex formulas, commands, and tools. Next, you’ll transition into SQL, becoming familiar with querying, exploring, and handling data from multiple sources. Lastly, you’ll dive into Python, learning the fundamentals of programming, statistical analysis, and data visualization. This progressive learning journey is designed for both aspiring data professionals and those looking to enhance their data skills.
Best of all, you’ll learn by doing — you’ll write code and get feedback directly in the browser. You’ll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Key skills
- Importing, preparing, analyzing, and summarizing data with Excel and Python
- Exploring querying, and extracting specific sets of data using SQL
- Transforming data into informative visual insights
- Using foundational math and statistics to perform data analysis
Path outline
Part 1: Introduction to Data Analysis with Excel [5 courses]
Introduction to Data Literacy and Data Analysis in Excel 4h
Objectives- Define, categorize, and evaluate data
- Identify ways data is used in business and everyday life
- Organize data
- Describe key steps of the data analysis process
Preparing Data in Excel 6h
Objectives- Import data into an Excel spreadsheet from more than one data source
- Organize data into a spreadsheet using worksheets and tables
- Clean, manipulate, and modify the data
- Consolidate the data for analysis
Visualizing Data in Excel 6h
Objectives- Visualize data using pie, column, histogram, line, scatter, and combo charts
- Select the appropriate data visualization
- Create charts using design principles
- Design visualizations for your intended audience
Exploring Data in Excel 5h
Objectives- Define descriptive statistics
- Identify the different types of descriptive statistics
- Identify when to use each descriptive statistic
- Apply and compare different descriptive statistics to a single column, to multiple columns, and to groups of data
- Create visualizations to explore and analyze data
Analyzing Data in Excel 5h
Objectives- Analyze data and discover business insights using PivotTables
- Identify trends over time in time-series data
- Summarize and visualize relationships between categorical and quantitative variables
- Confirm a relationship between an independent and dependent variable using linear regression
- Estimate the sensitivity of an output to given inputs
Part 2: Fundamentals of SQL [5 courses]
Introduction to SQL and Databases 5h
Objectives- Define the structure of SQL
- Create basic queries to extract data from tables in a database
- Define databases
- Identify different versions of SQL
- Write good SQL code
Summarizing Data in SQL 3h
Objectives- Employ SQL to compute statistics
- Provide statistics by group
- Filter results over groups
Combining Tables in SQL 4h
Objectives- Combine tables using inner joins
- Employ different types of joins
- Employ other SQL clauses with joins
- Join on complex conditions
- Employ set operators like UNION and EXCEPT
SQL Subqueries 6h
Objectives- Nest a query inside another query
- Employ different types of subqueries
- Employ common table expressions
- Scale your project with complex queries
Window Functions in SQL 7h
Objectives- Set up a frame for window functions
- Compute running aggregations with aggregate window functions
- Explore rank window functions
- Apply distribution window functions
- Use offset window functions
Part 3: Introduction to Python [4 courses]
Introduction to Python Programming 3h
Objectives- Write computer programs using Python
- Save values using variables
- Process numerical data and text data
- Create lists using Python
Basic Operators and Data Structures in Python 5h
Objectives- Use for loops to repeat processes and conduct data analysis
- Implement if, else, and elif statements in programming logic
- Employ logical and comparison operators in Python
- Develop and update Python dictionaries for data manipulation
- Construct frequency tables using dictionaries for data analytics
Python Functions and Jupyter Notebook 7h
Objectives- Write Python functions
- Debug functions
- Define function arguments
- Write functions that return multiple variables
- Employ Jupyter notebook
- Build a portfolio project
Intermediate Python for Data Science 8h
Objectives- Clean and analyze text data
- Define object-oriented programming in Python
- Process dates and times
Part 4: Data Wrangling and Visualization in Python [3 courses]
Introduction to Pandas and NumPy for Data Analysis 14h
Objectives- Improve your workflow using vectorized operations
- Select data by value using Boolean indexing
- Analyze data using pandas and NumPy
Introduction to Data Visualization in Python 8h
Objectives- Visualize time series data with line plots
- Define correlations and visualize them with scatter plots
- Visualize frequency distributions with bar plots and histograms
- Improve your exploratory data visualization workflow using pandas
- Visualize multiple variables using Seaborn's relational plots
Data Cleaning and Analysis in Python 11h
Objectives- Employ data aggregation techniques
- Combine datasets
- Transform and reshape data
- Clean strings and resolve missing data
Part 5: Statistics Fundamentals for Data Analysis in Python [2 courses]
Introduction to Statistics in Python 9h
Objectives- Sample data using simple random sampling, stratified sampling, and cluster sampling
- Measure variables in statistics
- Create frequency distribution tables
Intermediate Statistics in Python 8h
Objectives- Summarize a distribution using the mean, the weighted mean, the median, or the mode
- Measure the variability of a distribution using the variance and the standard deviation
- Compare values using z-scores
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Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.
We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.
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Go from zero to job-ready
Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.
Build your project portfolio
Build confidence with our in-depth projects, and show off your data skills.
Challenge yourself with exercises
Work with real data from day one with interactive lessons and hands-on exercises.
Showcase your path certification
Share the evidence of your hard work with your network and potential employers.
Projects in this path
Preparing Data with Excel
For this project, you’ll take on the role of a data professional in a marine biology research organization. You’ll use Excel to import, organize and clean a dataset on shark attacks, preparing it for an analysis team to study trends in attack location and frequency over time.
Visualizing the Answer to Stock Questions Using Spreadsheet Charts
For this project, you’ll step into the shoes of a business analyst to explore historical stock market data using Excel. By applying information design concepts, you’ll create compelling visualizations and craft an insightful report – building in-demand skills for communicating data-driven insights.
Identifying Customers Likely to Churn for a Telecommunications Provider
For this project, you’ll act as a data analyst for a telecommunications company. You’ll use Excel to explore customer data and build profiles of those likely to churn, applying statistics, PivotTables and charts.
Analyzing Retail Sales
For this project, we’ll take on the role of an analyst for a chain of retail stores to explore sales performance across categories and over time using Excel.
Analyzing Kickstarter Projects
For this project, you’ll assume the role of a data analyst at a startup considering launching a Kickstarter campaign. You’ll analyze data to help the team understand what might influence a campaign’s success.
Plus 9 more projects
Build your project portfolio with the Data Analyst in Python path.
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