Introduction to Data Science, Machine Learning & AI using Python
Data science is a field that has exploded in popularity in recent years, and for good reason. Companies across industries are increasingly relying on data to inform their decision-making, and skilled data scientists are in high demand. In this comprehensive course, you'll learn the foundational skills and techniques you need to succeed in this exciting field.
You'll start by exploring the role of a data scientist and the lifecycle of data science efforts within an organization. Then, you'll dive into the technical skills you need, such as using Python and its relevant libraries for data analysis and visualization, preprocessing unstructured data, and building AI/ML models.
You'll also explore key machine learning algorithms, including linear regression, decision tree classifiers, and clustering algorithms. And, you'll learn how to apply these techniques to real-world problems, such as predicting customer churn and building recommendation engines.
Throughout the data science training, you'll have the opportunity to work on hands-on exercises and projects, allowing you to practice your skills and build your portfolio. By the end of the course, you'll have a deep understanding of the data science process, the tools and techniques used by data scientists, and the ability to apply these skills to real-world problems.
In this course, you will:
- Translate everyday business questions and problems into Machine Learning tasks to make data-driven decisions
- Use Python Pandas, Matplotlib & Seaborn libraries to explore, analyze, and visualize data from various sources including the web, word documents, email, NoSQL stores, databases, and data warehouses
- Train a Machine Learning Classifier using different algorithmic techniques from the Scikit-Learn library, such as Decision Trees, Logistic Regression, and Neural Networks
- Re-segment your customer market using K-Means and Hierarchical algorithms for better alignment of products and services to customer needs
- Discover hidden customer behaviors from Association Rules and build a Recommendation Engine based on behavioral patterns
- Investigate relationships & flows between people and business-relevant entities using Social Network Analysis
- Build predictive models of revenue and other numeric variables using Linear Regression
- Gain access to an exclusive LinkedIn group for peer and community support
- Test your knowledge with the included end-of-course exam
- Leverage continued support with after-course one-on-one instructor coaching and computing sandbox
Data Science in Python Instructor-Led Course Outline
- Module 1: The Role of a Data Scientist: Combining Technical and Non-Technical Skills
- Module 2: Data Manipulation and Visualization using Python's Pandas and Matplotlib Libraries
- Module 3: Preprocessing and Analyzing Unstructured Data with Natural Language Processing
- Module 4: Linear Regression and Feature Engineering for Business Problem Solving
- Module 5: Classification Models and Evaluation for Predictive Analysis
- Module 6: Alternative Approaches to Classification and Model Evaluation
- Module 7: Clustering Techniques for Customer and Product Segmentation
- Module 8: Association Rules and Recommender Systems for Business Applications
- Module 9: Network Analysis for Organizational Insights
- Module 10: Big Data Analytics, Communication, and Ethics
