Data Science with Python Certification Course
- Offered byEdureka
Data Science with Python Certification Course at Edureka Overview
Duration | 10 weeks |
Total fee | ₹18,695 |
Mode of learning | Online |
Credential | Certificate |
Data Science with Python Certification Course at Edureka Highlights
- Earn Industry Recognised Certification
- Live Interactive Learning
- Lifetime Access
- 24x7 Support
- Hands-On Project Based Learning
- Cloud Lab
- No cost EMI option
Data Science with Python Certification Course at Edureka Course details
- For Programmers, Developers, Technical Leads, Architects
- For Developers aspiring to be a ?Machine Learning Engineer'
- For Analytics Managers who are leading a team of analysts
- For Business Analysts who want to understand Machine
- For Learning (ML) Techniques
- For Information Architects who want to gain expertise in
- For Predictive Analytics
- For Professionals who want to design automatic predictive models
- Programmatically download and analyze data
- Learn techniques to deal with different types of data ? ordinal, categorical, encoding
- Learn data visualization
- Using I python notebooks, master the art of presenting step by step data analysis
- Gain insight into the 'Roles' played by a Machine Learning Engineer
- Describe Machine Learning
- Work with real-time data
- Learn tools and techniques for predictive modeling
- Discuss Machine Learning algorithms and their implementation
- Validate Machine Learning algorithms
- Perform Text Mining and Sentimental analysis
- Explain Time Series and its related concepts
- Gain expertise to handle business in future, living the present
- Edureka's Data Science with Python Certification Course is accredited by NASSCOM, aligns with industry standards, and approved by the Government of India
- This course will help learner master important Python concepts such as data operations, file operations, and various Python libraries such as Pandas, Numpy, Matplotlib which are essential for Data Science
- This course is well suited for professionals and beginners
- This Python for Data Science certification training will also help to understand Machine Learning, Recommendation Systems, and many more Data Science concepts to help to get started with Data Science career
Data Science with Python Certification Course at Edureka Curriculum
Introduction to Python
Overview of Python
The Companies using Python
Different Applications where Python is Used
Discuss Python Scripts on UNIX/Windows
Values, Types, Variables
Operands and Expressions
Conditional Statements
Loops
Command Line Arguments
Writing to the Screen
Sequences and File Operations
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
Deep Dive ? Functions, OOPs, Modules, Errors and Exceptions
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
Introduction to NumPy, Pandas and Matplotlib
Data Analysis
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
Metadata for imported Datasets
Matplotlib library
Grids, axes, plots
Markers, colours, fonts and styling
Types of plots - bar graphs, pie charts, histograms
Contour plots
Data Manipulation
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
Introduction to Machine Learning with Python
Python Revision (numpy, Pandas, scikit learn, matplotlib)
What is Machine Learning?
Machine Learning Use-Cases
Machine Learning Process Flow
Machine Learning Categories
Linear regression
Supervised Learning - I
What is 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?
Dimensionality Reduction
Introduction to Dimensionality
Why Dimensionality Reduction
PCA
Factor Analysis
Scaling dimensional model
LDA
Supervised Learning - II
What is Naïve Bayes?
How Naïve Bayes works?
Implementing Naïve Bayes Classifier
What is a Support Vector Machine?
Illustrate how Support Vector Machine works?
Hyperparameter Optimization
Grid Search vs Random Search
Implementation of Support Vector Machine for Classification
Unsupervised Learning
What is Clustering & its Use Cases?
What is K-means Clustering?
How K-means algorithm works?
How to do optimal clustering?
What is C-means Clustering?
What is Hierarchical Clustering?
How Hierarchical Clustering works?
Association Rules Mining and Recommendation Systems
What are Association Rules?
Association Rule Parameters
Calculating Association Rule Parameters
Recommendation Engines
How Recommendation Engines work?
Collaborative Filtering
Content Based Filtering
Reinforcement Learning
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
Values
Time Series Analysis
What is Time Series Analysis?
Importance of TSA
Components of TSA
White Noise
AR model
MA model
ARMA model
ARIMA model
Stationarity
ACF & PACF
Model Selection and Boosting
What is Model Selection?
Need of Model Selection
Cross ? Validation
What is Boosting?
How Boosting Algorithms work?
Types of Boosting Algorithms
Adaptive Boosting
Statistical Foundations (Self-Paced)
What is Exploratory Data Analysis?
EDA Techniques
EDA Classification
Univariate Non-graphical EDA
Univariate Graphical EDA
Multivariate Non-graphical EDA
Multivariate Graphical EDA
Heat Maps
Data Connection and Visualization in Tableau (Self-paced)
Data Visualization
Business Intelligence tools
VizQL Technology
Connect to data from File
Connect to data from Database
Basic Charts
Chart Operations
Combining Data
Calculations
Advanced Visualizations (Self-paced)
Trend lines
Reference lines
Forecasting
Clustering
Geographic Maps
Using charts effectively
Dashboards
Story Points
Visual best practices
Publish to Tableau Online
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