Data Scientist Training
HR ANALTICS
Training Summary
This comprehensive Data Scientist training program is designed to equip participants with the skills and knowledge necessary to extract valuable insights from data, build predictive models, and drive data-driven decision-making. The course will cover a wide range of topics, including:
- Data Collection and Cleaning: Gathering, cleaning, and preparing data for analysis.
- Data Exploration and Visualization: Exploring data using statistical techniques and creating insightful visualizations.
- Statistical Analysis: Applying statistical methods to analyze data and draw meaningful conclusions.
- Machine Learning: Building predictive models using various machine learning algorithms, including regression, classification, clustering, and deep learning.
- Data Mining: Discovering hidden patterns and relationships within large datasets.
- Big Data Analytics: Processing and analyzing massive amounts of data using tools like Hadoop and Spark.
- Natural Language Processing (NLP): Extracting insights from textual data.
- Computer Vision: Analyzing visual data, such as images and videos.
- Collect and Clean Data: Gather, clean, and prepare data for analysis.
- Explore and Visualize Data: Use data visualization techniques to communicate insights effectively.
- Apply Statistical Analysis: Utilize statistical methods to analyze data and draw conclusions.
- Build Machine Learning Models: Develop predictive models using various machine learning algorithms.
- Discover Hidden Patterns: Apply data mining techniques to uncover valuable insights.
- Process Big Data: Handle and analyze large datasets efficiently.
- Extract Insights from Textual Data: Apply NLP techniques to analyze textual data.
- Analyze Visual Data: Use computer vision techniques to analyze images and videos.
- Make Data-Driven Decisions: Use data analytics to inform strategic decision-making.
- Communicate Data Insights: Effectively communicate data insights to both technical and non-technical audiences.
- Stay Updated on Data Science Trends: Keep up-to-date with the latest advancements in data science.
Training Objectives
Upon completion of this training, participants will be able to:
By achieving these objectives, participants will be well-prepared to tackle complex data science challenges and drive innovation in various industries.
The program will cover
By combining Agile Project Management and Scrum Master training, participants will gain a comprehensive understanding of Agile principles and practices, enabling them to lead and manage successful Agile projects.
Statistics
- Introduction to Statistics and Why Statistics?
- Types of Statistics
- Statistical Visualizations
- Descriptive Statistics
- Distribution
- Inferential Statistics
Microsoft Excel for Data Analytics
- Introduction to Data Analytics
- Introduction to Microsoft Excel
- Microsoft Excel for Data Manipulation and Cleaning
- Microsoft Excel for Data Analysis
- Microsoft Excel for Dashboarding and Reporting
- Data Storytelling
- Predictive Analytics / Forecasting using Microsoft Excel
Tableau
- Introduction to Tableau
- Tableau Basics, Time Series, Aggregation, and Filters
- Understanding Aggregations, Granularity, and Level of Detail
- Work with Data Blending in Tableau
- Understand Types of Joins and How They Work
- Create Table Calculations and Working with Parameters
- Relationship vs Joins
- Creating Maps, Scatter Plot, Working with Hierarchies
- Building Dashboards and Creating Storylines
- Predictive Analytics / Forecasting using Tableau
SQL (Structured Query Language)
- Introduction to SQL
- SQL Commands
- SQL for Data Analytics
- Data Query Language
- Aggregate Functions
- Joins and Subqueries
Python Programming
- Getting Started with Python
- Data Types and Structures
- Comparison Operators and Conditional Statements
- For Loop and While Loop
- Functions
- Numpy
- Pandas
- Data Visualization (Matplotlib & Seaborn)
- ChatGPT for Coding
Machine Learning
- Introduction to Machine Learning
- Types of Machine Learning
- Exploratory Data Analysis
- Projects in Supervised Machine Learning
- Projects in Unsupervised Machine Learning
Computer Vision
- Image Processing using Numpy
- Image Processing using OpenCV
- Face, Eyes and Smile Detection Project
- Pose Detection
- Hand Detection
- Other Computer Vision Projects
Natural Language Processing (NLP)
- NLTK
- Vader and Other Algorithms
Microsoft Fabric
- Introduction to Microsoft Fabric
- Creating a Fabric Workspace and Lakehouse
- Writing SQL Queries in Fabric
- Building Power BI Reports in Fabric
