DataScience_Machine Learning - NLP- Python-R-BigData-PySpark

Data Science is a rapidly growing field, consistently ranked as the #1 Best Job in America – a position it has held for three years running according to Glassdoor (2018). If you're looking for a career with high demand and excellent prospects, a Data Science career might be perfect for you. Python is the leading programming language in Data Science, with R holding the second position. Data Science focuses on extracting meaningful insights from data, moving beyond traditional analysis methods to leverage data mining, programming, and specialized tools and algorithms.

Why Python for Data Science?

Python's popularity in Data Science is soaring. Researchers and developers utilize it extensively for everything from data cleaning and model training to building advanced AI and Machine Learning software. LinkedIn identifies Python as the most in-demand Data Science skill in the United States.

Data Science Training: Master Key Skills

Our comprehensive Data Science with R, Python, and Spark Training equips you with expertise in powerful Machine Learning Algorithms, including:

• K-Means Clustering
• Decision Trees
• Random Forest
• Naive Bayes

This Data Science Course provides a strong conceptual foundation in:

• Statistics
• Time Series Analysis
• Text Mining
• Deep Learning (introduction)

You'll gain practical experience by implementing these techniques in real-world scenarios across diverse industries such as:

• Media
• Healthcare
• Social Media
• Aviation
• Human Resources (HR)

Curriculum Highlights:

Introduction to Data Science

• Learning Objectives: Understand how Data Science analyzes large, unstructured data using various tools.
• Topics:
  • What is Data Science? What does it involve?
  • The Era of Data Science
  • Business Intelligence vs. Data Science
  • Data Science Life Cycle
  • Data Science Tools
  • Introduction to Big Data and Hadoop
  • Introduction to R
  • Introduction to Spark

Statistical Inference

• Learning Objectives: Learn essential statistical techniques and terminology for data analysis.
• Topics:
  • What is Statistical Inference?
  • Key Statistics Terminologies
  • Measures of Central Tendency
  • Measures of Dispersion
  • Probability
  • Normal Distribution
  • Binary Distribution

Data Extraction, Wrangling, and Exploration

• Learning Objectives: Learn to extract data from various sources, structure it, analyze it, and visualize it effectively.
• Topics:
  • Data Analysis Pipeline
  • Data Extraction Methods
  • Types of Data (Raw and Processed)
  • Data Wrangling Techniques
  • Exploratory Data Analysis (EDA)
  • Data Visualization
  • Data Representation

Introduction to Machine Learning

• Learning Objectives: Get a foundational understanding of Machine Learning and implement supervised learning algorithms.
• Topics:
  • What is Machine Learning?
  • Machine Learning Use Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Supervised Learning Algorithms:
    • Linear Regression
    • Logistic Regression
  • Define Data Science and its various stages.
  • Implement Data Science development methodology in business scenarios.
  • Identify areas of applications of Data Science.
  • Understand the fundamental concepts of Python.
  • Use various Data Structures of Python.
  • Perform operations on arrays using NumPy library.
  • Perform data manipulation using the Pandas library.
  • Visualize data and obtain insights from data using the Matplotlib and Seaborn library.
  • Apply Scrapy and Beautiful Soup to scrape data from websites.
  • Perform an end-to-end case study on data extraction, manipulation, visualization, and analysis using Python.