In this Data Science Training in Python course, you will learn how to use Python libraries to build, evaluate, and deploy Machine Learning (ML) and Artificial Intelligence (AI) models that can help you gain previously uncovered insights from your data.
This course covers every stage of the Data Science Lifecycle and teaches you how to manage, transform, and visualize raw data to create predictive models that will help you find and evaluate future opportunities.
Data Science Training in Python Delivery Methods
Data Science Training in Python Benefits
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
- What is the required skillset of a Data Scientist?
- Combining the technical and non-technical roles of a Data Scientist
- The difference between a Data Scientist and a Data Engineer
- Exploring the full lifecycle of Data Science efforts within the organization
- Turning business questions into Machine Learning (ML) and Artificial Intelligence (AI) models
- Exploring diverse and wide-ranging data sources that can be used to answer business questions
- Introducing the features of Python that are relevant to Data Scientists and Data Engineers
- Viewing Data Sets using Python’s Pandas library
- Importing, exporting, and working with all forms of data, from Relational Databases to Google Images
- Using Python Selecting, Filtering, Combining, Grouping and Applying Functions from Python’s Pandas library
- Dealing with Duplicates, Missing Values, Rescaling, Standardizing and Normalizing Data
- Visualizing data for both exploration and communication with the Pandas, Matplotlib and Seaborn Python libraries
- Preprocessing Unstructured Data such as web adverts, emails, and blog posts for AI/ML models
- Exploring the most popular approaches to Natural Language Processing (NLP) such as stemming and “stop” words
- Preparing a term-document matrix (TDM) of unstructured documents for analysis
- Expressing a business problem, such as customer revenue prediction, as a linear regression task
- Assessing variables as potential Predictors of the required Target (e.g., Education as a predictor of Salary Build)
- Interpreting and Evaluating a Linear Regression model in Python using measures such as RMSE
- Exploring the Feature Engineering possibilities to improve the Linear Regression model
- Learning how AI/ML Classifiers are built and used to make predictions such as Customer Churn
- Exploring how AI/ML Classification models are built using Training, Test, and Validation
- Evaluating the strength of a Decision Tree Classifier
- Examining alternative approaches to classification
- Considering how Activation Functions are integral to Logistic Regression Classifiers
- Investigating how Neural Networks and Deep Learning are used to build self-driving cars
- Exploring the probability foundations of Naive Bayes classifiers
- Reviewing different approaches to measuring the performance of AI/ML Classification Models
- Reviewing ROC curves, AUC measures, Precision, Recall, Confusion Matrices
- Uncovering new ways of segmenting your customers, products, or services using clustering algorithms
- Exploring what the concept of similarity means to humans and how it can be implemented programmatically through distance measures on descriptive variables
- Performing top-down clustering with Python’s Scikit-Learn K-Means algorithm
- Performing bottom-up clustering with Scikit-Learn’s hierarchical clustering algorithm
- Examining clustering techniques on unstructured data (e.g., Tweets, Emails, Documents, etc.)
- Building models of customer behaviors or business events from logged data using Association Rules
- Evaluating the strength of these models through probability measures of support, confidence, and lift
- Employing feature engineering approaches to improve the models
- Building a recommender for your customers that is unique to your product/service offering
- Analyzing your organization, its people, and environment as a network of inter-relationships
- Visualizing these relationships to uncover previously unseen business insights
- Exploring ego-centric and socio-centric methods of analyzing connections important to your organization
- Examining Cloud (Microsoft, Amazon, Google) approaches to handling Big Data analytics
- Exploring the communications and ethics aspects of being a Data Scientist
- Surveying the paths of continual learning for a Data Scientist
Unlimited Access Data Science in Python Premium Blended Training
The eBooks and on-demand courses provided as with this offering are a great way to explore your interest in the topics covered in the instructor-led course. At any time during your annual access to this offering, you may attend the 5-day instructor-led course or one of our 1-day review sessions, Introduction to Python for Data Analytics.
- Managing Data Science
- Hands-On Data Analysis with Pandas
- Hands-On Python Natural Language Processing
- Data Science Algorithms in a Week - Second Edition
- Machine Learning for Algorithmic Trading - Second Edition
- Hands-On Neural Networks
- Mastering Machine Learning Algorithms - Second Edition
- Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
- Hands-On Data Science for Marketing
- Python Feature Engineering Cookbook
- Artificial Intelligence By Example - Second Edition
- Hands-On Machine Learning on Google Cloud Platform
- Exploratory Data Analysis with Pandas and Python 3.x
- Advanced Data Structures and Algorithms in Python
