Course Highlights:
Discover how to address practical data science challenges by applying basic programming concepts, computational thinking, and data analysis approaches. Enroll in Data Science Training at Erode From The Training Trains To Advance Your Career in the World’s Most Demanding Skill.
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Data Science Syllabus
Statistics Essentials for Analytics
- Comprehending the Data
- The Applications of Probability
- Inference from Statistics
- Clustering of Data
- Verifying the Data
- Modeling Regression
Data Science Overview
- Data Science
- Data Scientists
- Examples of Data Science
- Python for Data Science
Data Analytics Overview
- An Overview of Data Science
- Procedures for Data Visualization
- Data Wrangling, Data Exploration, and Model Selection.
- EDA, or exploratory data analysis
- Hypothesis Building and Testing
- Plotting
- Hypothesis Building and Testing
Statistical Analysis and Business Applications
- Overview of 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
Data Visualization in Python using Matplotlib
Python: Environment Setup and Essentials
- Overview of the Anaconda
- Anaconda Python Distribution Installation for Windows, Mac Os and Linux
- Installation of Jupyter Notebooks
- Jupyter Notebook Overview
- Differential Assignment
- Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
- Creating, accessing, and slicing tuples
- Creating, accessing, and slicing lists
- Creating, viewing, accessing and modifying dicts
- Establishing and utilizing set operations
- Basic Operators: *, +, and in
- Functions
- Control Flow
Mathematical Computing with Python (NumPy)
- Overview of NumPy
- Features, Uses, 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
- Functions Universal (ufunc)
- Manipulation of Shape
- Broadcasting
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
- SciPy sub-packages – I O
Python integration with Hadoop, MapReduce and Spark
- Need for Integrating Python with Hadoop
- Big Data Hadoop Architecture
- MapReduce
- ClouderaQuickStart VM Set Up
- Apache Spark
- Resilient Distributed Systems (RDD)
- PySpark
- Spark Tools
- PySpark Integration with Jupyter Notebook
Data Science with Python Web Scraping
- Web Scraping
- Common Data/Page Formats on The Web
- The Parser
- value of Objects
- Knowing the Tree
- Searching the Tree
- Navigating options
- Modifying the Tree
- Parsing Only Part of the Document
- Printing and Formatting
- Encoding
Data Manipulation with Python (Pandas)
- Overview to Pandas
- Data Structures
- Series
- DataFrame
- Missing Values
- Data Operations
- Data Standardization
- Pandas File Read and Write Support
- SQL Operation
Machine Learning with Python (Scikit–Learn)
- Overview of Machine Learning
- Method of Machine Learning
- How Learning Models, Both Supervised and Unsupervised, Function
- Scikit-Learn
- Supervised Learning Models – Linear Regression
- Supervised Learning Models: Logistic Regression
- K Nearest Neighbors (K-NN) Model
- Unsupervised Learning Models: Clustering
- Unsupervised Learning Models: Dimensionality Reduction
- Pipeline
- Model Persistence
- Model Evaluation – Metric Functions
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
Trainer Profile
At Training Trains, we are proud of our group of exceptionally skilled and knowledgeable instructors who are committed to offering top-notch Python data science instruction. This is a brief overview of our trainers’ profiles:
Industry Experience: With a wealth of knowledge in Python programming and Data Science, our trainers are professionals from the industry. They have experience working on actual projects, have acquired real-world knowledge, and are aware of the difficulties and current trends in the sector.
Subject Matter Experts:Our instructors are specialists in Python and Data Science. They are extremely knowledgeable about the ideas, instruments, and methods applied in Data Science initiatives.
Teaching Experience: Our trainers have been teaching for a long time and have developed their teaching techniques over that time.
Up to Date with the Latest Trends: Our instructors keep up with the most recent developments and trends in Python and Data Science. In order to ncorporate new developments, industry practices, and emerging technologies into the training curriculum, they regularly update their knowledge and skills.
Mentors and Guides : Our trainers go beyond just delivering lectures. They act as mentors and guides, providing individual attention and support to each participant.
Career Advancement: Earning a Python certification in Data Science can greatly improve your chances of landing a good job. Since certifications guarantee a candidate’s proficiency using Python for data analysis, machine learning, and other data science tasks, employers frequently favor candidates who hold them.