Otavio

“The learning paths on Dataquest are incredible. They give you a direction through the learning process – you don’t have to guess what to learn next.”

Otávio Silveira

Data Analyst @ Hortifruti

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 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 Course 3h

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 5h

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 5h

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 13h

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

The Dataquest guarantee

Guarantee

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.

Money

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.

Master skills faster with Dataquest

Go from zero to job-ready

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 your project portfolio

Build confidence with our in-depth projects, and show off your data skills.

Challenge yourself with exercises

Challenge yourself with exercises

Work with real data from day one with interactive lessons and hands-on exercises.

Showcase your path certification

Showcase your path certification

Impress employers by completing a capstone project and certifying it with an expert review.

Projects in this path

Guided Project: Preparing Data with Excel

In this guided project, you’ll import a dataset, organize it, clean it, and then consolidate it into a single table, preparing it for analysis.

Guided Project: Visualizing the Answer to Stock Questions Using Spreadsheet Charts

In this project, we will apply the knowledge and skills we developed in the preceding lessons to present stock data clearly and help answer important questions about the underlying data.

Guided Project: Identifying Customers Likely to Churn for a Telecommunications Provider

In this project, we’ll conduct an Exploratory Data Analysis (EDA) on data from a telecommunications provider to create profiles on customers that are at risk of churn.

Guided Project: Analyzing Retail Sales

Work with retail sales data to explore trends and relationships. Build basic models to confirm the statistical significance of your insights.

Project: Learn and Install Jupyter Notebook

Learn the basics of Jupyter Notebook

Plus 7 more projects

Build your project portfolio with the Data Analyst in Python path.

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Aaron

Aaron Melton

Business Analyst at Aditi Consulting

“Dataquest starts at the most basic level, so a beginner can understand the concepts. I tried learning to code before, using Codecademy and Coursera. I struggled because I had no background in coding, and I was spending a lot of time Googling. Dataquest helped me actually learn.”

Jessi

Jessica Ko

Machine Learning Engineer at Twitter

“I liked the interactive environment on Dataquest. The material was clear and well organized. I spent more time practicing then watching videos and it made me want to keep learning.”

Victoria

Victoria E. Guzik

Associate Data Scientist at Callisto Media

“I really love learning on Dataquest. I looked into a couple of other options and I found that they were much too handhold-y and fill in the blank relative to Dataquest’s method. The projects on Dataquest were key to getting my job. I doubled my income!”

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