Data Science Online Training

Course Content

PART – I 

Introduction to Data Science

• What is Data Science? Why we need it?
• Applications of Data Science
• Data Science Life Cycle

Data Collection and Preprocessing

• Understanding Business Requirement
• Data Types
• Data Collection Sources
• Data Collection Methods
• Web Scraping
• Data Cleaning & Preprocessing techniques
• Data Visualization using various plots
• -- Line Plot
• -- Bar Plot
• -- Histogram
• -- Pie Chart
• -- Scatter Plot
• -- Box Plot
• -- Heat Map, etc
• Handling Missing Data
• Exploratory Data Analysis (EDA) with Use case
  

Descriptive Statistics

• Types of Data & Scales of Measurements
• Measures of Central Tendency
• Measures of Variability
• Skewness and Kurtosis
• Population & Sample
• Normal & Sampling Distributions

Inferential Statistics

• Central Limit Theorem and Confidence Interval
• Hypothesis Testing
• ANOVA
• Simple Linear Regression and Correlation
• Nonparametric Methods and Chi-Square Tests

PART – II

Machine Learning – Mining Relationships and Filtering (Unsupervised)

• i. Dimensionality Reduction
• 2. Cluster Analysis
• 3. Model performance Metrics (Performance Evaluation)
• 4. Combining Methods: Ensembles and Uplifting Modeling
• 5. Associate Rules and Collaborative Filtering

Forecasting, Time Series

• 1. Types of Forecasting
• 2. Trend Analysis
• 3. Seasonality and Cyclical Behavior
• 4. Stationarity
• 5. AR, MA, ARIMA Models
• 6. Smoothing Methods

PART - III

Machine Learning– Prediction and Classification Methods (Supervised)

• 1. Multiple Linear Regression
• 2. k-Nearest Neighbors (kNN)
• 3. Naïve Bayes Classifier
• 4. Regression and Classification Trees
• 5. Logistic Regression
• 6. Neural Nets
• 7. Discriminant Analysis
• 8. Support Vector Machines

PART – IV 

Text Mining & NLP

• 1.Introduction to Text analytics and NLP
• 2. Applications of NLP
• 3. Regular Expressions
• 4. Tokenization
• 5. Stemming & Lemmatization
• 6. Bag of Words
• 7. POS tagging
• 8. DTM
• 9. TF-IDF
• 10. NER
• 11. Sentiment Analysis with Use case

Softwares & Tools: Python, R, Tableau, Excel

Course Packages

Batches

Interview Questions

Career Scope