Python Certification Training for Data Science
Pinnacledu’s Python programming certification course enables you to learn Python from scratch. This Python Course will also help you master important Python programming concepts such as data operations, file operations, object-oriented programming and various Python libraries such as Pandas, Numpy, Matplotlib which are essential for Data Science. Pinnacledu’s Python Certification Training course is also a gateway towards your Data Science career.
Course Features
- Lectures 114
- Quizzes 0
- Duration 50 hours
- Skill level All levels
- Language English
- Students 64738
- Assessments Yes
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Introduction to Python
Learning Objectives: You will get a brief idea of what Python is and touch on the basics.
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Sequences and File Operations
Learning Objectives: Learn different types of sequence structures, related operations and their usage. Also learn diverse ways of opening, reading, and writing to files.
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Deep Dive – Functions, OOPs, Modules, Errors and Exceptions
Learning Objectives: In this Module, you will learn how to create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.
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Introduction to NumPy, Pandas and Matplotlib
Learning Objectives: This Module helps you get familiar with basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Also, you will learn in detail about data visualization.
- 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
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Data Manipulation
Learning Objective: Through this Module, you will understand in detail about Data Manipulation
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Introduction to Machine Learning with Python
Learning Objectives: In this module, you will learn the concept of Machine Learning and its types.
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Supervised Learning - I
Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
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Dimensionality Reduction
Learning Objectives: In this module, you will learn about the impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing LDA model.
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Supervised Learning - II
Learning Objectives: In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
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Unsupervised Learning
Learning Objectives: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.
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Association Rules Mining and Recommendation Systems
Learning Objectives: In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm.
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Reinforcement Learning
Learning Objectives: In this module, you will learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by. You will be able to define an optimal solution for an agent based on agent-environment interaction.
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Time Series Analysis
Learning Objectives: In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught different models for time series modeling such that you analyze a real time-dependent data for forecasting.
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Model Selection and Boosting
Learning Objectives: In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn on how to convert weaker algorithms into stronger ones.
