Data Science with Python Certification Training Course
Learn from the Best, Learn from TopD
Features of This Course
Why Choose Data Science with Python Certification Training Course?
Data Science with Python Certification Training Course by TopD Learning is curated by industry professionals based on what is common for the industry and what the industries demand and need.
TopD Learning’s Data Science with Python course will help you learn the basics of Data Science from scratch and master important Python programming concepts such as data operations, file operations, object-oriented programming, and various Python libraries such as Pandas, Numpy, Matplotlib essential for Data Science.
Data Science with Python Certification Training Course will also let you understand the various types of Machine Learning, Recommendation Systems, and many more Data Science concepts to help you get started with your Data Science career.
Course Curriculum
- Overview
- The Companies using Python
- Different Applications where it is used
- Discuss Python Scripts on UNIX/Windows
- Values, Types, Variables
- Operands and Expressions
- Conditional Statements
- Loops
- Command Line Arguments
- Writing to the screen
- Python files I/O Functions
- Numbers
- Strings and related operations
- Tuples and related operations
- Lists and related operations
- Dictionaries and related operations
- Sets and related operations
- Functions
- Function Parameters
- Global Variables
- Variable Scope and Returning Values
- Lambda Functions
- Object-Oriented Concepts
- Standard Libraries
- Modules Used in Python
- The Import Statements
- Module Search Path
- Package Installation Ways
- Errors and Exception Handling
- Handling Multiple Exceptions
- NumPy – arrays
- Operations on arrays
- Indexing slicing and iterating
- Reading and writing arrays on files
- Pandas – data structures & index operations
- Reading and Writing data from Excel/CSV formats into Pandas
- matplotlib library
- Grids, axes, plots
- Markers, colours, fonts and styling
- Types of plots – bar graphs, pie charts, histograms
- Contour plots
- Basic Functionalities of a data object
- Merging of Data objects
- Concatenation of data objects
- Types of Joins on data objects
- Exploring a Dataset
- Analysing a dataset
- Revision (numpy, Pandas, scikit learn, matplotlib)
- What is Machine Learning?
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- Linear regression
- Gradient descent
- What are Classification and its use cases?
- What is Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Perfect Decision Tree
- Confusion Matrix
- What is Random Forest?
- Introduction to Dimensionality
- Why Dimensionality Reduction
- PCA
- Factor Analysis
- Scaling dimensional model
- LDA
- What is Naïve Bayes?
- How Naïve Bayes works?
- Implementing Naïve Bayes Classifier
- What is Support Vector Machine?
- Illustrate how Support Vector Machine works?
- Hyperparameter Optimization
- Grid Search vs Random Search
- Implementation of Support Vector Machine for Classification
- What is Clustering & its Use Cases?
- What is K-means Clustering?
- How does K-means algorithm work?
- How to do optimal clustering
- What is C-means Clustering?
- What is Hierarchical Clustering?
- How Hierarchical Clustering works?
- What are Association Rules?
- Association Rule Parameters
- Calculating Association Rule Parameters
- Recommendation Engines
- How does Recommendation Engines work?
- Collaborative Filtering
- Content-Based Filtering
- What is Reinforcement Learning
- Why Reinforcement Learning
- Elements of Reinforcement Learning
- Exploration vs Exploitation dilemma
- Epsilon Greedy Algorithm
- Markov Decision Process (MDP)
- Q values and V values
- Q – Learning
- α values
- What is Time Series Analysis?
- Importance of TSA
- Components of TSA
- White Noise
- AR model
- MA model
- ARMA model
- ARIMA model
- Stationarity
- ACF & PACF
- What is Model Selection?
- The need for Model Selection
- Cross-Validation
- What is Boosting?
- How Boosting Algorithms work?
- Types of Boosting Algorithms
- Adaptive Boosting
Data Science with Python Training Course Features
Instructor-led Live Sessions
We use only the finest instructors in the IT industry with good experience. Learn from our instructor and interact live at your desired place via virtual learning programs scheduled to run at specific times.
E-Learning Self-Paced Training
We offer self-paced training programs, which are structured in modules so as to offer maximum flexibility to those who wish to work around their already hectic schedules.
One to One Training
We offer is one to one training as a mode of educational training where you can Interact one to one with the instructor to get a fully focused training experience. It is preferred by students who prefer a personalized approach.
24 x 7 Expert Support
We have a lifetime 24x7 online support team to resolve all your technical queries, through a ticket based tracking system.
Certification
After successfully completing your course & projects, TopD Learning will provide a professional certification for you.
Lifetime Access
You will get lifetime access to our LMS where quizzes, presentations & class recordings are available.
Course Completion Certification
Give your resume a BOOST, and join Top Companies with a good package.
You will receive a course completion certificate post completing all assignments & tasks certifying that you have learned the skills and completed the course successfully.
Frequently Asked Questions
FAQs
You will never miss a lecture at TopD Learning! We’ve got you covered:
- View the recorded session of the class available in your LMS.
- You can attend the missed session, in any other live batch.
