**Data Science with Python and Julia**

- Python introduction
- Installing Pycharm, Pydev, Anaconda, Python,
- Python datatypes
- Integer, String
- List
- Tuple
- Set
- Dictionary
- Run time Values: input() / raw_input()
- Conditions (if, simple if, chained conditions etc)
- Python strings (len / strip /range /
- Formatting outputs
- Loops (while, for loop)
- Installing packages(pip / conda)
- Numpy (Numpy Operations)
- Pandas (Pandas Operations, data frames)
- What is a Plot?
- Matplotlib
- Def (functions)
- Classes (__init__ , self, __ del_ )
- __main__
- Python OOPS
- Super
- Stack and queues
- Basics of Julia
- Installing a Julia Working Environment
- Working with Variables and data Types
- Controlling the Flow
- Diving Deeper Into Julia
- Using Types and Parameterized Methods
- Optimizing Code by Using and Writing Macros
- Code in Modules
- Working with the Package Ecosystem
- Working With Data In Julia
- Reading and Writing Data Files and Julia Data
- Using DataArrays and DataFrames
- The Power of DataFrames
- Basics Statistics With Julia
- Exploring and Understanding a Dataset Statistically
- Overview of the Plotting Techniques in Julia
- Visualizing Data with Scatterplots, Histograms, and Box Plots
- Basics of Math’s
- Addition subtraction in Algebra
- Addition subtraction multiple terms
- The invisible 1
- Multiplication and Division of negative numbers
- Division Negative Numbers
- Multiplication and Division in Algebra
- Probability
- Principles of Probability
- Bayes Theorem
- Sigma Notation
- Delta
- Gamma
- Vectors
- Magnitude
- Basics of statistics
- Descriptive Statistics
- Inferential Statistics
- Average, Mode
- SD. Corrélation
- Mean, Mode
- Médian, Skewness
- Variance
- Standard Deviation
- Covariance
- Correlation
- Regression
- Anova
- R-square
- F test, WOE, VIF
- Hypothesis Testing
- Research Hypothesis
- Statistical Hypothesis
- Substantive Hypothesis
- Basics of Data Analysis
- Types of Data
- 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
- Use case of Cadbury chocolate?
- Why are sales down
- Descriptive
- Diagnostic
- Predictive
- Prescriptive
- Normalisation
- st Level, 2nd Level, 3rd Level, BCNF

**Artificial Intelligence**

- What is Artificial Intelligence?
- Founders of A.I
- History of A.I
- 7 Aspects of A.I
- STRONG A.I
- WEAK A.I
- A.I as a Threat?
- A.I Use cases and Applications
- SINGULARITY
- I.Q Levels
- Basics of Hadoop/ Big data
- Evolution of data
- What is this big data?
- Hadoop as a solution
- Hadoop Ecosystem
- HDFS and its Architecture
- Data ware Housing Basics(ETL: Extract, Transform, Load)
- What is the Need of BI ?
- What is Data warehousing
- Key Terminologies
- OLTP Vs OLAP
- ETL
- DATA MART
- TYPES OF DATAMART’s
- METADATA
- Creating a DataMart
- SQL Basics
- CRUD
- DDL
- DML
- DCL
- TCL
- DQL
- Create/select/update/alter/delete/truncate/rollback/commit etc
- Aggregate Functions(SUM/MIN/MAX/AVG)
- Basic Queries
- Machine Learning
- Basics of ML
- SCIKIT learn
- Small example prediction
- Machine learning Types
- How does machine Learning works

**Types of Machine Learning**

- Supervised
- Unsupervised
- Reinforcement
- Over fitting and Underfitting
- Bias and Variance

**What is Regression?**

- What is Classification?
- Classification Algorithms?
- Linear Regression
- Linear Formula, R-square, adjusted R-square
- How to calculate mean / mean square
- Designing Regression Line (using Maths and Stats)
- Algorithm and use case (Practical Prediction)
- Logistic Regression
- What is Logistic Regression?
- Linear vs Logistic Regression
- Data wrangling
- Algorithm
- Use with Prediction (Practical)
- Decision Tree
- Creating decision tree
- CART algorithm
- Decision Tree Terminologies
- Gini Index, Information Gain
- Reduction Variance, Chi Square
- How to choose best Attribute?
- Entropy?
- What is impurity?
- How to build Decision tree
- How to build a Root node, Leaf node
- What is Pruning?
- Practical Example on Decision Tree (ML)
- Random Forests
- Working with Random forests Practical example (Prediction)
- Naive Bayes
- Bayes Theorem
- Loading datasets
- Practical example (Prediction)
- SVM (Support Vector Machine)
- What is a Hyperplane?
- How to load datasets
- Practical example on SVM (ML)
- KNN Algorithm’
- What is KNN?
- Advantages of KNN
- Disadvantages of KNN
- Predicting with Example (ML)
- K means Clustering
- What is Clustering?
- Diff b/w Clustering and Classification
- What is centroid?
- What is Euclidean Distance?
- Types of Clustering
- How K means Clustering works?
- Use case with prediction (Practical)

**Deep Learning**

- Introduction to Deep Learning
- Why do we need deep learning?
- Applications of Deep learning
- What is a Neural Network?
- What is Biological neural?
- Introduction to Artificial Neural Networks
- Difference b/w Biological neural and Artificial Neural Network
- Work with Activation Functions (Sigmoid, Threshold, ReLU, and Hyperbolic)
- Introduction to Tensor flow
- What are Tensors?
- Tensor ranking
- Types of Tensors
- Image Classifications with Tensorflow I
- Image Classifications with Tensorflow II
- Face recognition with Tensor flow
- CNN (Convolutional Neural Networks) algorithm
- Practical use case
- RNN (Recurrent Neural Networks) algorithm
- Practical Use case
- Project 1 (Machine Learning)
- Project 2 (Deep Learning
- Project 3 (live project)