DeepLearning.AI Data Analytics

Overview

Learn in-demand analytics skills that can transform your career. Data-informed decision-making is now an essential skill for everyone, from everyday consumer choices to business decisions at all levels.

As reliance on data grows, so does the need for professionals who can analyze and interpret it effectively. The Data Analytics Professional Certificate, led by industry leader Sean Barnes, equips you with the skills to manage the entire data lifecycle, from defining problems to delivering insights.

The skills you'll gain are in high demand, and with data science roles projected to grow 36% from 2023 to 2033 according to the U.S. Bureau of Labor Statistics, developing these skills puts you at the forefront of a data-centric world.

Unique to this program is its integration of new AI tools into the analytics workflow. You'll learn to use large language models as a thought partner, accelerating tasks like simulation modeling, formula debugging, and data visualization.

Each of the course examples comes from real-world use cases, building practical and immediately useful skills. Whether you're a software engineer working with data pipelines, a marketer or business analyst extracting insights, or building a career in data analysis, you'll gain the foundation to excel in the data economy.

This program blends core statistical methods with AI-assisted workflows, perfect for beginner data professionals or experienced practitioners looking for fresh techniques.

Syllabus

• Introduction to Data Analytics
Overview of Analytics and Data-Driven Decision Making
The Role of Data Analytics in Various Industries
Understanding the Data Lifecycle
• Core Concepts in Statistical Methods
Descriptive Statistics
Inferential Statistics
Probability Distributions
Hypothesis Testing
• AI-Assisted Workflows
Introduction to Large Language Models (LLMs)
Integrating AI Tools in the Analytics Process
Using AI for Simulation Modeling
AI-Assisted Formula Debugging
Enhancing Data Visualization with AI Tools
• Data Management and Preprocessing
Data Cleaning and Transformation
Handling Missing Data
Feature Engineering
Introduction to Data Pipelines and ETL Processes
• Advanced Analytics Techniques
Exploratory Data Analysis (EDA)
Predictive Modeling
Machine Learning Basics
Introduction to Text Analytics and Natural Language Processing
• Real-World Use Cases and Applications
Case Studies in Business and Marketing Analytics
Data-Driven Decision Making in Healthcare
Applications in Financial Analytics
Data Science in Product Development
• Communicating Insights and Results
Effective Data Visualization Techniques
Storytelling with Data
Creating Impactful Data Reports
Crafting Visual Presentations for Business Impact
• Capstone Project
Defining a Real-World Problem
Collecting and Preparing Data
Analyzing Data and Extracting Insights
Presenting Findings and Recommendations
• Career Development in Data Analytics
Identifying Career Paths in Data Science
Building a Professional Portfolio
Networking and Professional Growth Opportunities

---

Taught by

Sean Barnes

---

Subjects

Data Science