Data science
4.5 /5- Offered bySSDN Technologies
Data science at SSDN Technologies Overview
Duration | 40 hours |
Mode of learning | Online |
Credential | Certificate |
Data science at SSDN Technologies Highlights
- A certificate from SSDN Technologies of successful completion of the course
- World Class Highly skilled and Certified Trainers with great mathematical and analytical skills
- Our eLearning method helps you to access Course material from anywhere from any device
- Access to various Big Data models
- Mock interview questions and sessions with best in field experts
- There are countless benefits of joining Data Science training with placement
Data science at SSDN Technologies Course details
- Recently graduated college students.
- This course targets medium level Python programmers who would like to dive deeper into the language.
- Data Analyst who want to upgrade their Data Scientist Level.
- Marketing executives to understand customers behaviour to come up with different promotions and offers.
- Learn the latest techniques to solve your business problems using machine learning, big data and deep learning.
- Build a solid foundation of knowledge in Data Science and Machine Learning with our expert faculty.
- Complete Project based on real time scenario
- Build your career as a Data Scientist in Analytics
- Understand the concept of Data Science and its use.
- Introduction of python libraries
- Understanding python Arrays with NumPy
- Use Of pandas.
- Understanding data visualizing.
- Algorithms
- Data Science training will make student an expert in data analytics using the latest programming languages
- The course will make learner enable to utilize their Data Science skills in making uniformed business decisions
- Data Science training program is designed to learn different types of statistical method and use them to create business strategy, road maps and various business models for organization’s growth.
Data science at SSDN Technologies Curriculum
Module 1: Data Science Overview
Data Science
Data Scientists
Examples of Data Science
Python for Data Science
Module 2: Data Analytics Overview
Introduction to Data Visualization
Processes in Data Science
Data Wrangling, Data Exploration, and Model Selection
Exploratory Data Analysis or EDA
Data Visualization
Plotting
Hypothesis Building and Testing
Module 3: Statistical Analysis and Business Applications
Introduction to Statistics
Statistical and Non-Statistical Analysis
Some Common Terms Used in Statistics
Data Distribution: Central Tendency, Percentiles, Dispersion
Histogram
Bell Curve
Hypothesis Testing
Chi-Square Test
Correlation Matrix
Inferential Statistics
Module 4: Python: Environment Setup and Essentials
Introduction to Anaconda
Installation of Anaconda Python Distribution - For Windows, Mac OS, and Linux
Jupyter Notebook Installation
Jupyter Notebook Introducti
Control Flow
Module 5: Mathematical Computing with Python (NumPy)
NumPy Overview
Properties, Purpose, and Types of ndarray
Class and Attributes of ndarray Object
Basic Operations: Concept and Examples
Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
Copy and Views
Universal Functions (ufunc)
Shape Manipulation
Broadcasting
Linear Algebra
Module 6: Scientific computing with Python (Scipy)
SciPy and its Characteristics
SciPy sub-packages
SciPy sub-packages –Integration
SciPy sub-packages – Optimize
Linear Algebra
SciPy sub-packages – Statistics
SciPy sub-packages – Weave
Module 7: Data Manipulation with Python (Pandas)
Introduction to Pandas
Data Structures
Series
DataFrame
Missing Values
Data Operations
Data Standardization
Pandas File Read and Write Support
SQL Operation
Module 8: Machine Learning with Python (Scikit–Learn)
Introduction to Machine Learning
Machine Learning Approach
How Supervised and Unsupervised Learning Models Work
Scikit-Learn
Supervised Learning Models - Linea
Unsupervised Learning Models: Dimensionality Reduction
Pipeline
Model Persistence
Model Evaluation - Metric Functions
Module 9: Natural Language Processing with Scikit-Learn
NLP Overview
NLP Approach for Text Data
NLP Environment Setup
NLP Sentence analysis
NLP Applications
Major NLP Libraries
Scikit-Learn Approach
Scikit - Learn Approach Built - in Modules
Scikit - Learn Approach Feature Extraction
Bag of Words
Extraction Considerations
Scikit - Learn Approach Model Training
Scikit - Learn Grid Search and Multiple Parameters
Pipeline
Module 10: Data Visualization in Python using Matplotlib
Introduction to Data Visualization
Python Libraries
Plots
Matplotlib Features:
Line Properties Plot with (x, y)
Controlling Line Patterns and Colors
Set Axis, Labels, and Legend Properties
Alpha and Annotation
Multiple Plots
Subplots
Types of Plots and Seaborn
Module 11: Data Science with Python Web Scraping
Web Scraping
Common Data/Page Formats on The Web
The Parser
Importance of Objects
Understanding the Tree
Searching the Tree
Navigating options
Modifying the Tree
Parsing Only Part of the Document
Printing and Formatting
Encoding
Module 12: Python integration with Hadoop, MapReduce and Spark
Need for Integrating Python with Hadoop
Big Data Hadoop Architecture
MapReduce
Cloudera QuickStart VM Set Up
Apache Spark
Resilient Distributed Systems (RDD)
PySpark
Spark Tools
PySpark Integration with Jupyter Notebook
Data science at SSDN Technologies Entry Requirements
- Core python programming in depth.
- Have attended or studied the Introduction to Python course , or possess.
- Not mentioned
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