When does class start/end?
Class hours may vary, please reach out to contact@ascendientlearning.com if you have any questions.
Applied Data Science Concepts with Python
Build a practical data-science foundation with Python that transfers directly to production work. The course covers the Python data-science environment, the data-science workflow with feature...
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$1,665USD
Duration
2 days
Course Code
DS-1201
Available Formats
Classroom
Overview
Course Description
Build a practical data-science foundation with Python that transfers directly to production work. The course covers the Python data-science environment, the data-science workflow with feature engineering and ethical considerations, and data wrangling through EDA, cleaning, and summarization. Probability, statistics, and hypothesis testing cover descriptive and inferential methods, distributions, p-values, and common tests. Scikit-learn workflows anchor the predictive-AI portion through algorithms, evaluation, cross-validation, and hyperparameter tuning. Hands-on labs produce a Jupyter-and-Pandas environment, an EDA report with bias and PII audit, a hypothesis-tested analysis, and trained scikit-learn classifiers. The course is designed for data scientists, analysts, and software developers who already know Python and want a complete data-science foundation.
Skills Gained
By the end of this course, participants will be able to:
- Apply standard data-science libraries to increase code reuse and portability
- Clean and preprocess data to uncover actionable business insights
- Visualize data to communicate strategic trends effectively
- Configure AI models that improve operational efficiency
- Apply advanced AI techniques to gain competitive advantage
Who Can Benefit
This course is designed for:
- Data Scientists & Analysts
- Software Developers
Prerequisites
Participants should enter this course with:
- Practical Python Experience
Organizational Objectives
This course assists organizations to:
- Build baseline data-science capability across analytics and engineering teams
- Reduce time-to-insight through standardized EDA and visualization patterns
- Lower model-development cost through scikit-learn-driven workflows
- Establish a shared data-science vocabulary across the organization
Software
All attendees must have a modern web browser and an Internet connection.
Course Details
Course Details
Module 1 - Python for Data Science
By the end of this module, you will be able to set up a Python environment for data science, navigate the data-science tools landscape, work in Jupyter Notebooks, and use Pandas Series and DataFrames for practical analysis.
- Introduction to Python for Data Science
- Overview of Data Science Tools
- Setting Up the Environment
- Jupyter Notebooks
- Practical Work with Pandas Series and DataFrames
- Hands-on Lab: Set up a Python data-science environment and use Jupyter Notebooks to work through a Pandas exercise.
Module 2 - Data Science Fundamentals
By the end of this module, you will be able to articulate what data science is, walk the data-science workflow, recognize ethical considerations, manipulate data with Pandas, and apply feature engineering to raw data.
- What is Data Science?
- Data Science Workflow
- Ethical Considerations in Data Science
- Data Manipulation with Pandas
- Data Types and Structures
- Feature Engineering
- Hands-on Lab: Identify PII and bias in a dataset and generate synthetic data to address gaps.
Module 3 - Data Wrangling & Visualization
By the end of this module, you will be able to perform exploratory data analysis with Pandas, clean and summarize data, combine data wrangling with visual EDA, and produce univariate and multivariate plots.
- Exploratory Data Analysis (EDA) with Pandas
- Data cleaning and summarizing
- Visual EDA combined with data wrangling
- Multivariate plots
- Univariate plots
- Detecting relationships and trends
- Hands-on Lab: Run EDA for data quality and produce a visual EDA report on a real-world dataset.
Module 4 - Probability, Statistics, and Hypothesis Testing
By the end of this module, you will be able to apply descriptive and inferential statistics, work with probability distributions, formulate null and alternative hypotheses, interpret p-values, and run common statistical tests.
- Introduction to Descriptive Statistics
- Inferential Statistics Overview
- Understanding Probability Distributions
- Formulating Null and Alternative Hypotheses
- Understanding P-Values and Statistical Significance
- Common statistical tests
- Hands-on Lab: Calculate descriptive and inferential statistics and run a hypothesis test on real data.
Module 5 - Basics of Predictive AI
By the end of this module, you will be able to define machine learning, AI, predictive AI, and generative AI, walk the ML workflow end-to-end, prepare data, train and evaluate models, and recognize the challenges of machine learning in production.
- Defining machine learning, AI, predictive AI, and generative AI
- Overview of neural networks
- The machine learning workflow
- Data collection, exploration, and preprocessing
- Model training, evaluation, and selection
- Model deployment, monitoring, and challenges
- Hands-on Lab: Walk through a complete machine-learning workflow on a sample dataset, deciding when AI is the right tool.
Module 6 - Predictive AI with Scikit-Learn
By the end of this module, you will be able to apply scikit-learn to predictive AI tasks, work with its key algorithms, prepare data for scikit-learn, evaluate models, and deploy predictive models with cross-validation and hyperparameter tuning.
- Getting started with scikit-learn for predictive AI
- Important algorithms in scikit-learn
- Data preparation for scikit-learn
- Evaluating models in scikit-learn
- Deploying predictive models with scikit-learn
- Using cross-validation and tuning hyperparameters
- Hands-on Lab: Apply scikit-learn to clustering and anomaly detection on a real-world dataset.
Schedule
FAQ
Does the course schedule include a Lunchbreak?
Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.
What languages are used to deliver training?
Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.
What does GTR stand for?
GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.
How do I find an Ascendient Learning training location?
We have training locations across the United States and Canada - View a complete list of classroom training locations.
Which delivery formats are available?
At Ascendient Learning, we offer training that is Instructor-Led, Online, Virtual, and Self-Paced.
Does Ascendient Learning deliver group training?
Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.
What does vendor-authorized training mean?
As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.
Is the training too basic, or will you go deep into technology?
It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.
How up-to-date are your courses and support materials?
We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.
Are your instructors seasoned trainers who have deep knowledge of the training topic?
Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.
Do you provide hands-on training and exercises in an actual lab environment?
Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.
Will you customize the training for our company’s specific needs and goals?
We will work with you to identify training needs and areas of growth. We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.
How do I get started with certification?
Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.
Will I get access to content after I complete a course?
You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.
How do I request a W9 for Ascendient Learning?
View our filing status and how to request a W9.
Reviews
this class was informative, made me think about certifying for the suse manager cert.
Overall ExitCertified is a great training provider and the remote learning is as effective as in person.
the course is good, covers many aspects, wish the lab is a little bit more in depth
Both course material and instructor demonstrated a sound foundation on Maximo material
This is my second course with ExitCertified. This course exceeded my expectations. The teacher was great and the class was fun.
