Data Science with Python
- Introduction to Data Science
- Brief Background in Python Or Unix
- Jupyter and Numpy
- Pandas
- Visualization
- Mini Project
- Machine Learning
- Working With Text and Databases
- Final Project
Python introduction
- Installing Pycharm, Pydev, Anaconda, Python
Python data types
- Integer, String
- List
- Tuple
- Set
- Dictionary
- Conditions
- Loop
- Numpy
- Pandas
- What is a Plot?
- Matplotlib
- Def (functions)
- stack and queues
Basics of Maths
- Vectors
- Magnitude
Basics of statistics
- Mean
- Median
- Arithmetic Mean
- Geometric Mean
- Harmonic Mean
- What is Plotting?
Basics of Data Analysis
- Data types
- Data life cycle
- Data analysis introduction
- Diff B/w Data analysis and Data Analytics
- Diff B/w AI / ML / DL
- Scientific notation(conversion)
- Basics of measurements
- Basics of designing graphs
- Basics of designing plots
- Labelling and graphs
- How to work with format certifications
Levels of Data Analysis
- Descriptive
- Diagnostic
- Predictive
- Prescriptive
Machine Learning
- Basics of ML
- scikit learn
- Small example prediction
- Machine learning Types
- How does machine learning works
- Regression and Classifications
- Classifications
- IRIS data sets
- Classification Algorithms
- Decision Tree
Decision Tree
- Creating a decision tree
- CART algorithm
- Decision Tree Terminologies
- Gini Index, Information Gain
- Reduction Variance, Chi-Square
KNN Algorithm ‘
- What is KNN?
- Advantages of KNN
- Disadvantages of KNN
- Predicting with Example(ML)
Linear Regression
- Logistic Regression
- Clustering
PROJECT1 (ML)
- Deep Learning using TensorFlow(Google)
- Introduction to Deep Learning
- Introduction to Artificial Neural Networks
- Introduction to Tensorflow
- what are Tensors
- Tensor ranking
- Types of Tensors
- Image Classifications with Tensorflow I
- Image Classifications with Tensorflow II
- Face reorganization with Tensor flow
- Activation Functions in a Neural network explained
- CNN (Convolution Neural Networks) algorithm
- RNN( Recurrent Neural Networks for Language modelling
- Gated Recurrent Units(GRUs), LSTMs
- Recursive Neural network
Adv Deep Learning
- NLP
- NLP Terminology
- NLP with Deep Learning
Project 2
- IT Systems Analyst
- Healthcare Data Analyst
- Operations Analyst
- Data Scientist
- Data Engineer
- Quantitative Analyst
- Data Analytics Consultant
- Digital Marketing Manager
- Project Manager
- Transportation Logistics Specialist
Data Science Using R Training
- Data Science Introduction & Use Cases
- Python Basics: Basic Syntax, Data Structures
- Python Basics: Loops, If-elif statements, Functions, Exception Handling
- Statistics, Measures of central tendency, Population, Sample, Probability Distribution, Normal and Binomial Distribution, Random Variable, Pictorial Representations
- Python Advanced: Numpy, Pandas
- Python Advanced: Data Manipulation, Matplotlib
Machine Learning
- ML Introduction & Use Cases
- Statistics 2 – Inferential Statistics
- Linear Regression
- Logistic Regression
- Decision Trees, Random Forest
- Modelling Techniques (PCA, Feature Engineering)
- KNN, Naive Bayes
- Support Vector Machines(SVM)
- Clustering, K-means
Deep Learning With NLP
- Introduction to NLP & Deep Learning
- Word Embeddings
- Word window classification
- Introduction to Artificial Neural Networks
- Introduction to Tensorflow
- Recurrent Neural Networks for Language modelling
- Gated Recurrent Units(GRUs), LSTMs
- Recursive Neural network
Advanced Machine Learning
- Market Basket Analysis & Apriori Algorithm
- Recommendation System
- Dimensionality Reduction
- Anomaly Detection
- XG Boost
- Gradient Boosting Machine(GBM)
- Stochastic Gradient Descent(SGD)
- Ensemble Learning
Data Analytics With R
- Data Science Introduction & Use Cases
- R Basics: Basic Syntax, Variable assignment, Data Types-numeric, string, boolean
- R Basics: Vectors, Matrices, Factors, Data Frames, ListsLoops, If-elif statements, Functions, Exception Handling
- Statistics, Measures of central tendency, Population, Sample, Probability Distribution, Normal and Binomial Distribution, Random Variable, Pictorial Representations
- R Advanced: Libraries
- R Advanced: Data Manipulation, plots
- Exploratory Data Analysis: Data Cleaning, Data Wrangling
