Python

• • What is Python?Importance of Python in Data Science
  • Python Installation Guidelines
  • Numerical parameters to represent data
  • NumPy for mathematical computing
  • Scientific computing with Python
  • Text, color map, markers, widths with Matplotlib.
  • Importing and exporting datasets in Python
  • Feature Engineering: Feature Selection and Extraction
  • Practice Tests on Python

Statistical Analysis

• • Statistical parameters to represent data
  • Apply function for parallel processing with Python
  • Statistical and Non-statistical Analysis
  • Population and Sample
  • Statistical Analysis Process,
  • Data Distribution
  • Hands-on Assignment, Real Scenarios, MCQs,
  • Practice Tests on , Statistics
  • Gradient Descent in Boosting Algorithms
  • Multiple linear regression
  • Linear regression vs Logistic regression
  • Understanding Naïve Bayes, Bayes theorem
  • Clustering - K-Means & Hierarchical
  • Hands-on Assignment, Real Scenarios, MCQs,
  • Practice Tests on Machine Learning

Machine Learning

• • Types of Machine Learning
  • What is Supervised, Unsupervised and Reinforcement
  • Decision trees and Random Forest
  • Advantages of tree-based models?
  • Algorithms used in Machine Learning techniques
  • Difference between Data Science, Machine Learning
  • and AI
  • Gradient Descent in Boosting Algorithms
  • Multiple linear regression
  • Linear regression vs Logistic regression
  • Understanding Naïve Bayes, Bayes theorem
  • Clustering - K-Means & Hierarchical
  • Hands-on Assignment, Real Scenarios, MCQs,
  • Practice Tests on Machine Learning

AI,Deep Learning, Natural Language Processing

• • Working with Neural Network
  • Functioning & Usage
  • Convolutional Networks
  • Recurrent Neural Networks
  • AutoEncoders
  • Long Short Term Memory
  • Deep learning with Keras
  • What is TensorFlow?
  • Deep learning with TensorFlow
  • Gradient Descent in Neural Networks
  • Defining and composing models, and deploying
  • TensorBoard.
  • Important Parameters of Perceptron
  • Recursive Neural Tensor Network theory
  • Hands-on Assignment, Real Scenarios, MCQs, Practice
  • Tests on Deep Learning & NLP

Data Science training Certification Course Roadmap

• What all do we cover in our Data Science Courses

  The Data Science learning path that you get to cover at JanBask Training is very informative and engaging. It has been prepared after vigorous market research on the trends of Data Science, industry needs, etc. Take a look at the things that we cover in this course.

  Data Science

  • What is Data Science?
  • Data Science Life Cycle
  • What is Machine Learning?
  • What is Business Analytics?
  • What is Artificial Intelligence?
  • How is data science different from BI and Reporting
  • End to End Data Science Project Life Cycle
  • knowledge of the concepts of data collection & data mining.

  R with Data Science

  • Why R and importance of R in Analytics
  • Installation of R and R-studio
  • Working Directories
  • Data Types, Operators, Loops-For and While
  • If-else statements, Nested statements
  • Working with Vector and Matrices
  • Reading and Writing Data in R
  • Working with Data, Manipulating Data
  • Objects, and Vectors

  Data Science with Python

  • Why Python for data science?
  • Overview of Python- Starting with Python
  • Installation of Python
  • Python Editors & IDE’s
  • Understand Jupyter notebook & Customize Settings
  • Concept of Packages/Libraries
  • Installing & loading Packages & Name Spaces
  • Data Types & Data objects/structures
  • List and Dictionary Comprehensions
  • Control flow & conditional statements

  Statistics Analysis

  • Definition and computation of the probability
  • Measurement of central tendencies and its applications
  • Spreads, Distributions(Normal, Z-distribution, Binomial, Poisson)
  • Various types of probability distributions(Continuous and discrete)
  • Measures of Central Tendencies and Variance
  • Sampling and Sampling distributions
  • Measures of shape( Skewness and Kurtosis)
  • Measures of the relationship between variables(Correlation, causation)
  • Hypothesis Testing(t-test, Chi-square, Anova)
  • Measures of Dispersion( Variance, std. deviation, Range)
  • Prediction and Confidence interval-Computation and Analysis
  • Correlation, Covariance, and Causation

  Machine Learning - Supervised

  • Supervised Learning
  • Algorithms in Supervised learning
  • Regression & Classification
  • Regression vs classification
  • Computation of correlation coefficient and Analysis
  • Multivariate Linear Regression Theory
  • Coefficient of determination (R2) and Adjusted R2
  • Model Misspecifications
  • Economic meaning of a Regression Model
  • Bivariate Analysis
  • Naive Baye classifier, Model Training
  • ANOVA (Analysis of Variance)

  Machine Learning - Unsupervised

  • What is Clustering
  • Supervised vs Unsupervised learning
  • Data Mining Process
  • Measure of distance
  • Hierarchical Clustering / Agglomerative Clustering
  • Non-clustering, K-Means Clustering
  • dimension reduction
  • Advantages of PCA
  • Calculation of PCA weights
  • Definition of a network (the LinkedIn analogy)
  • The measure of Node strength in a Network
  • What is Market Basket / Affinity Analysis
  • The measure of distance/similarity between users
  • Pre-processing, corpus Document-Term Matrix (DTM) and TDM

  Deep Learning

  • Why is Deep Learning taking off?
  • Advantage of Deep Learning
  • What is the difference between ML, DL and AI?
  • Why is deep learning important?
  • Sharing Variables
  • Activation Functions
  • Illustrate Perceptron
  • Training a Perceptron
  • Neural Networks with TensorFlow
  • Convolutional Neural Networks (CNN)
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN
  • Recurrent Neural Networks (RNN)

  Natural Language Processing

  • What is Natural Language Processing?
  • What Can Developers Use NLP Algorithms For?
  • Open Source NLP Libraries
  • Topic Modeling
  • Sentiment Extraction
  • Lexicons and Emotion Mining

  Big Data Hadoop

  • Hadoop Installation and Setup
  • Hadoop ecosystem components
  • Hadoop’s Key Characteristics
  • What is Big Data & its analytics
  • Hadoop Ecosystem and HDFS
  • Hadoop Core Components
  • Hadoop Cluster and its Architecture
  • Rack Awareness and Block Replication
  • YARN and its Advantage
  • MapReduce Framework and Pig
  • Apache Spark Next-Generation Big Data Framework
  • How Spark differs from other frameworks?

  Scala

  • What is Scala
  • Scala in other Frameworks
  • Introduction to Scala REPL
  • Basic Scala Operations
  • Variable Types in Scala
  • Control Structures in Scala
  • Understanding the constructor overloading,
  • Various abstract classes
  • The hierarchy types in Scala,
  • Foreach loop, Functions and Procedures
  • Collections in Scala- Array

  Spark

  • Overview to Spark
  • Spark installation, Spark configuration,
  • Spark Components & its Architecture
  • Spark Deployment Modes
  • Limitations of MapReduce in Hadoop
  • Working with RDDs in Spark
  • Introduction to Spark Shell
  • Deploying Spark without Hadoop
  • Parallel Processing
  • Spark MLLib - Modelling BigData with Spark

  Apache Kafka & Flume

  • what is Kafka, Why Kafka,
  • Configuring Kafka Cluster
  • Kafka architecture
  • Producing and consuming messages
  • Operations, Kafka monitoring tool
  • Need of Apache Flume
  • What is Apache Flume
  • Understanding the architecture of Flume
  • Basic Flume Architecture

  Data Visualization with Tableau

  • What is Data Visualization
  • Overview to Tableau 10.0
  • Installing Tableau, Establishing Connection
  • Tableau interface
  • Connecting to DataSource
  • Installation of Tableau Desktop
  • Architecture of Tableau
  • Connection to Excel, cubes, and PDFs
  • Data extraction, Data blending
  • Calculations to your workbook
  • Mapping data in Tableau
  • Custom Geocoding, Polygon Maps.
  • Web Mapping Services.
  • Background Images.
  • Dashboards and Stories