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DeepLearning.AI

Data Analytics Professional Certificate

  • up to 5 weeks
  • Beginner

Build job-ready data analytics skills from the ground up, with no prior data experience required. Learn to augment core statistical techniques with cutting-edge AI-assisted workflows, extract meaningful insights from real-world datasets, and prepare for a role in data analytics.

  • Data Analysis
  • Data Visualization
  • Statistical Techniques
  • Python Programming
  • AI Integration in Analytics

Overview

This comprehensive program equips you with the skills to manage the entire data lifecycle, from defining problems to delivering insights. You'll learn foundational analysis and visualization, statistical techniques, Python for automation, and data extraction from various sources. The course also covers AI integration in analytics, preparing you for the growing demand in data-centric roles.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    English
    course language
  • Professional Certification
    upon course completion
  • Self-paced
    course format
  • Live classes
    delivered online

Who is this course for?

Aspiring Data Analysts

Build the essential skills needed to land roles like Data Analyst, Business Intelligence Analyst, Operations Analyst, or Data Specialist positions.

Software Engineers

Enhance your technical toolkit with data analysis capabilities to build more insightful applications or transition to data-focused engineering roles.

Marketing Professionals

Add data-driven decision making that transforms how you develop campaigns and measure performance.

Gain job-ready skills in data analytics, a field projected to grow significantly. This course covers essential topics like data visualization, statistical techniques, and AI integration, making it ideal for beginners and professionals looking to enhance their analytical toolkit.

Pre-Requisites

1 / 3

  • No prior data experience required

  • Basic understanding of statistics is helpful

  • Familiarity with spreadsheets is beneficial

What will you learn?

Data and the Data Analyst Role
Define data and its common representations, differentiate between structured vs. unstructured data, and explore data roles and their business impact.
Using Spreadsheets for Data Analytics
Describe spreadsheet use cases, perform basic tasks, and connect different file types to structured/unstructured data.
Data Visualization
Understand data storytelling, create and interpret visualizations, and construct data stories using analytics best practices.
The Data Analytics Lifecycle
Define stages in the data analysis lifecycle, gather business context, and explain how domain knowledge affects analysis.
Foundational Statistical Techniques
Define population, sample, and sampling methods, analyze data using measures of central tendency, and perform segmentation with group-by analysis.
Probability and Simulation
Explain the role of probability, apply probability rules, and simulate random variates for business decisions.
Confidence Intervals
Calculate and interpret confidence intervals to estimate population parameters and use LLMs for inferential statistical techniques.
Hypothesis Testing
Conduct single-variable tests, control error rates, and describe types of hypothesis testing available to data analysts.
Getting started with Python
Set up a Python notebook environment, create variables, implement lists and tuples, and write custom functions.
Data structures and descriptive statistics
Create and manipulate DataFrames using pandas, calculate basic descriptive statistics, and create basic visualizations.
Visualization
Use Pandas and popular Python packages to generate visuals and develop custom visualizations.
Inferential statistics
Construct confidence intervals, conduct t-tests, and develop linear regression models using Python.
Time series
Model dates and times, apply descriptive statistics, and develop time series forecasting models.
Web scraping & text processing
Use Pandas to scrape structured data, apply string methods for cleaning, and use Beautiful Soup for HTML parsing.
APIs
Describe APIs, convert JSON responses into dataframes, and handle paginated API responses.
Databases & SQL
Define databases, write basic SQL queries, and use SQL within Python to access and analyze databases.
Preprocessing, validation, and joins with SQL
Use SQL to filter data, perform data validation, and combine data from multiple tables using joins.
Data storytelling fundamentals
Craft a compelling narrative arc for a data story and select the best type of media for communication.
Creating Charts in Tableau
Select appropriate data-driven insights, apply visualization types, and write compelling summaries.
Creating dashboards & stories in Tableau
Explain dashboard design best practices, create static and interactive visualizations, and deploy dashboards.
The job search
Explain how to build a strong data analytics portfolio.

Meet your instructor

  • Sean Barnes

    Instructor, Netflix

    Sean Barnes is a Data Science & Engineering Leader at Netflix. His research interests include infectious disease modeling, healthcare and sports analytics, agent-based and discrete-event simulation, machine learning, and data visualization.

Upcoming cohorts

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$49