3-4 Months Training’s

Data Scientist Course

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  1. Artificial Intelligence Training
  2. Data Analyst Training
  3. Data Science Training
  4. Data Science with Deep Learning Training

Artificial Intelligence Training

Learning Objectives: Get an introduction to AI and have a solid foundation of the core fundamentals

  • Advanced search
  • Constraint satisfaction problems
  • Knowledge representation and reasoning
  • Non-standard logics
  • Uncertain and probabilistic reasoning (Bayesian networks, fuzzy sets)
  • Foundations of semantic web: semantic networks and description logics
  • Rules systems: use and efficient implementation
  • Planning systems

Learning Objectives: You will get an idea of what Python is and touch on the basics


  • Overview of Python
  • The Companies using Python
  • Different Applications where Python is used
  • Discuss Python Scripts on UNIX/Windows
  • Values, Types, Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Command Line Arguments
  • Writing to the screen

Hands On/Demo:

  • Creating “Hello World” code
  • Variables
  • Demonstrating Conditional Statements
  • Demonstrating Loops


  • Fundamentals of Python programming

Learning Objectives: In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data. At the end of this module, you should be able to:

  • Define Unsupervised Learning
  • Discuss the following Cluster Analysis
  • K – means Clustering
  • Hierarchical Clustering

Hands On:

  • Implementing K-means Clustering
  • Implementing Hierarchical Clustering
  • Implementing DBSCAN

Learning Objectives: This Module helps you get familiar with basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Also, you will learn in detail about data visualization.


  • NumPy – arrays
  • Operations on arrays
  • Indexing slicing and iterating
  • Reading and writing arrays on files
  • Pandas – data structures & index operations
  • Reading and Writing data from Excel/CSV formats into Pandas
  • matplotlib library
  • Grids, axes, plots
  • Markers, colours, fonts and styling
  • Types of plots – bar graphs, pie charts, histograms
  • Contour plots

Hands On/Demo:

  • NumPy library- Creating NumPy array, operations performed on NumPy array
  • Pandas library- Creating series and dataframes, Importing and exporting data
  • Matplotlib – Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of Plot


  • Probability Distributions in Python
  • Python for Data Visualization

Learning Objectives: In this module, you will be introduced to data and its types and accordingly sample data and derive meaningful information from the data in terms different statistical parameters. At the end of this Module, you should be able to:

  • Understand various data types
  • Learn Various variable types
  • List the uses of variable types
  • Explain Population and Sample
  • Discuss sampling techniques
  • Understand Data representation


  • Introduction to Data Types
  • Numerical parameters to represent data
  • Mean
  • Mode
  • Median
  • Sensitivity
  • Information Gain
  • Entropy
  • Statistical parameters to represent data


  • Estimating mean, median and mode using python
  • Calculating Information Gain and Entropy

Learning Objective: In this module, you should learn about probability, interpret & solve real-life problems using probability. You will get to know the power of probability with Bayesian Inference. At the end of this Module, you should be able to:

  • Understand rules of probability
  • Learn about dependent and independent events
  • Implement conditional, marginal and joint probability using Bayes Theorem
  • Discuss probability distribution
  • Explain Central Limit Theorem


  • Uses of probability
  • Need of probability
  • Bayesian Inference
  • Density Concepts
  • Normal Distribution Curve


  • Calculating probability using python
  • Conditional, Joint and Marginal Probability using Python
  • Plotting a Normal distribution curve

Learning Objective: In this module, you will explore text classification, vectorization techniques and processing using scikit-learn


  • Machine Learning: Brush Up
  • Bag of Words
  • CountVectorizer
  • Term Frequency (TF)
  • Inverse Document Frequency (IDF)
  • Converting text to features and labels
  • Naive Bayes Classifier
  • SVM Classifier
  • Topic Modeling using LDA
  • NN Classifier
  • Leveraging Confusion Matrix

Hands On/Demo:

  • Demonstrate Bag of Words Approach
  • Working with CountVectorizer ()
  • Using TF & IDF
  • Converting text to features and labels
  • Demonstrate text classification using Multinomial NB Classifier
  • Demonstrate LDA for Topic modelling
  • Demonstrate SVM for text Classification
  • Demonstrate NN for text classification
  • Leveraging Confusion Matrix

Data Analyst Training

Data Science Training

Data Science with Deep Learning Training

These trainings are meant for conducting in colleges. If your college would like us to conduct drop an Enquiry at info@myanalyticsmentor.com.

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