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Batch
Date: Mar 9th @ 10:00AM
Faculty: Mr. Sharma
Duration : 2 Months
Fee: 15,000/-INR + Reg Fee 100/-INR
Location
: Maitrivanam, Hyderabad.
Venue
:
DURGA SOFTWARE SOLUTIONS at Maitrivanam
Plot No : 202,
IInd Floor ,
HUDA Maitrivanam,
Ameerpet, Hyderabad-500038.
Ph.No: +91 - 9246212143, 80 96 96 96 96
Syllabus:
Data Science
SIGNIFICANCE OF DATA SCIENCE
R – PROGRAMMING LANGUAGE
Lesson 1 - R basics, R-studio
Lesson 2 - Data structures
Lesson 3 - R Programming fundamentals
Lesson 4 - Working with Data
Lesson 5 - Strings and Dates
Lesson 6 - Sorting, merging data with R
Lesson 7 - Working with dplyr, reshape2, tidyr packages
Lesson-9 - Data Visualization with R (ggplot2)
Lesson 8 - Statistics with R
PYTHON – PROGRAMMING LANGUAGE
Lesson 1 - Python Fundamentals, Jupyter Notebook IDE
Lesson 2 - Python Basic Data types and Collections
Lesson 3 - Working with Functions, Modules, Packages
Lesson 4 - Object Oriented Programming
Lesson 5 - Working with NumPy
Lesson 6 - Working with Pandas
Lesson 7 - Working with Scikit-learn
Lesson-8 - Data visualization (matplotlib, seaborn)
Lesson-9 - Statistics with Python
STATISTICS
Lesson 1 - Introduction
Lesson 2 - Sample or population data
Lesson 3 - The fundamentals of descriptive statistics
Lesson 4 - Measures of central tendency, asymmetry, and variability
Lesson 5 - Practical example: descriptive statistics
Lesson 6 - Distributions
Lesson 7 - Estimators and estimates
Lesson 8 - Confidence intervals: advanced topics
Lesson 9 - Practical example: Inferential Statistics
Lesson 10 - Hypothesis testing: Introduction
Lesson 11 - Practical examples: hypothesis testing
MACHINE LEARNING
Lesson 1: Introduction to Artificial Intelligence and Machine Learning
Lesson 2: Data Wrangling and Manipulation
Lesson 3: Supervised Learning - Regression
Lesson 4: Feature Engineering
Lesson 5: Supervised Learning - Classification
Lesson 6: Unsupervised learning
Lesson 7: Time Series Modeling
Lesson 8: Ensemble Learning
Lesson 9: Recommender Systems
Lesson 10: NLP
DEEP LEARNING
Lesson 1: Introduction to neural networks, ANN and deep learning
Lesson 2: Understanding ANN, RNN, CNN, RCNN
Lesson 3: Feed Forward, Backward propagation
Lesson 4: Gradient and stochastic gradient in neural networks
Lesson 5: Tensor flow
Lesson 6: Keras
ADDITIONAL THINGS:
