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Edureka Data Science Masters Certification Course#

Course Information#

Edureka Data Science Masters Certification Course

  • Instructor: Edureka

  • Duration: 10 weeks

  • Level: Intermediate

Table of Contents#

Data Science Masters Program

Index

Python Statistics for Data Science Course#

Index Course Outline 1.: Understanding the Data Topics: • Introduction to Data Types • Numerical parameters to represent data. a. Mean b. Mode c. Median d. Sensitivity e. Information Gain f. Entropy • Statistical parameters to represent data. 2.: Probability and its uses Topics: • Uses of probability • Need of probability • Bayesian Inference Discover Learning

Python Statistics for Data Science Course (Self-paced)

• Density Concepts • Normal Distribution Curve 3.: Statistical Inference Topics: • Point Estimation • Confidence Margin • Hypothesis Testing • Levels of Hypothesis Testing 4.: Data Clustering Topics: • Association and Dependence • Causation and Correlation • Covariance • Simpson’s Paradox • Clustering Techniques 5.: Testing the Data Topics: • Parametric Test • Parametric Test Types • Non- Parametric Test • Experimental Designing

6.: Regression Modelling Topics: • Logistic and Regression Techniques • Problem of Collinearity • WOE and IV • Residual Analysis • Heteroscedasticity

Python#

1.: Introduction to Python Topics: • 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 2.: Sequences and File Operations Topics: • Python files I/O Functions • Numbers • Strings and related operations Discover Learning

Data Science with Python Certification Course

• Tuples and related operations • Lists and related operations • Dictionaries and related operations • Sets and related operations 3.: Deep Dive – Functions, OOPs, ,. Errors and Exceptions Topics: • Functions • Function Parameters • Global Variables • Variable Scope and Returning Values • Lambda Functions • Object Oriented Concepts • Standard Libraries • .Used in Python • The Import Statements • S.earch Path • Package Installation Ways • Errors and Exception Handling • Handling Multiple Exceptions 4.: Introduction to NumPy, Pandas and Matplotlib Topics: • Data Analysis • NumPy - arrays • Operations on arrays

• Reading and writing arrays on files • Pandas - data structures & index operations • Reading and Writing data from Excel/CSV formats into Pandas • Metadata for imported Datasets. • Matplotlib library • Grids, axes, plots • Markers, colours, fonts and styling • Types of plots - bar graphs, pie charts, histograms • Contour plots 5.: Data Manipulation Topics: • Basic Functionalities of a data object • Merging of Data objects • Concatenation of data objects • Types of Joins on data objects • Exploring a Dataset • Analysing a dataset 6.: Introduction to Machine Learning with Python Topics: • Python Revision (numpy, Pandas, scikit learn, matplotlib) • What is Machine Learning? • Machine Learning Use-Cases • Machine Learning Process Flow • Machine Learning Categories

7.: Supervised Learning - I Topics: • What is Classification and its use cases? • What is Decision Tree? • Algorithm for Decision Tree Induction • Creating a Perfect Decision Tree • Confusion Matrix • What is Random Forest? 8.: Dimensionality Reduction Topics: • Introduction to Dimensionality • Why Dimensionality Reduction • PCA • Factor Analysis • Scaling dimensional model • LDA 9.: Supervised Learning - II Topics: • What is Naïve Bayes? • How Naïve Bayes works? • Implementing Naïve Bayes Classifier • What is a Support Vector Machine? • Illustrate how Support Vector Machine works? • Hyperparameter Optimization

• Implementation of Support Vector Machine for Classification 1.0: Unsupervised Learning Topics: • What is Clustering & its Use Cases? • What is K-means Clustering? • How K-means algorithm works? • How to do optimal clustering? • What is C-means Clustering? • What is Hierarchical Clustering? • How Hierarchical Clustering works? 1.1: Association Rules Mining and Recommendation Systems Topics: • What are Association Rules? • Association Rule Parameters • Calculating Association Rule Parameters • Recommendation Engines • How Recommendation Engines work? • Collaborative Filtering • Content Based Filtering 1.2: Reinforcement Learning Topics: • What is Reinforcement Learning? • Why Reinforcement Learning?

• Exploration vs. Exploitation dilemma • Epsilon Greedy Algorithm • Markov Decision Process (MDP) • Q values and V values • Q – Learning • Values 1.3: Time Series Analysis Topics: • What is Time Series Analysis? • Importance of TSA • Components of TSA • White Noise • AR model • MA model • ARMA model • ARIMA model • Stationarity • ACF & PACF 1.4: Model Selection and Boosting Topics: • What is Model Selection? • Need of Model Selection • Cross – Validation • What is Boosting? • How Boosting Algorithms work?

• Adaptive Boosting 1.5: Statistical Foundations (Self-paced) Topics: • What is Exploratory Data Analysis? • EDA Techniques • EDA Classification • Univariate Non-graphical EDA • Univariate Graphical EDA • Multivariate Non-graphical EDA • Multivariate Graphical EDA • Heat Maps 1.6: Data Connection and Visualization in Tableau (Self-paced) Topics: • Data Visualization • Business Intelligence tools • VizQL Technology • Connect to data from File • Connect to data from Database • Basic Charts • Chart Operations • Combining Data

1.7: Advanced Visualizations (Self-paced) Topics: • Trend lines • Reference lines • Forecasting • Clustering • Geographic Maps • Using charts effectively • Dashboards • Story Points • Visual best practices

• Vectors, and how to build them using Arrays and Linked Lists with Pointers Course Outline 1.: Introduction to Big Data Hadoop and Spark Topics: • What is Big Data? • Big Data Customer Scenarios • Limitations and Solutions of Existing Data Analytics Architecture with Uber Use Case • How Hadoop Solves the Big Data Problem? • What is Hadoop? • Hadoop’s Key Characteristics • Hadoop Ecosystem and HDFS • Hadoop Core Components • Rack Awareness and Block Replication • YARN and its Advantage • Hadoop Cluster and its Architecture • Hadoop: Different Cluster Modes • Big Data Analytics with Batch & Real-Time Processing • Why is Spark Needed? • What is Spark? • How Spark Differs from its Competitors? Discover Learning

PySpark Certification Training Course#

• Spark at eBay • Spark’s Place in Hadoop Ecosystem 2.: Introduction to Python for Apache Spark Topics: • Overview of Python • Different Applications where Python is Used • Values, Types, Variables • Operands and Expressions • Conditional Statements • Loops • Command Line Arguments • Writing to the Screen • Python files I/O Functions • Numbers • Strings and related operations • Tuples and related operations • Lists and related operations • Dictionaries and related operations

3.: Functions, OOPS, and .in Python Topics: • Spark Components & its Architecture • Spark Deployment Modes • Introduction to PySpark Shell • Submitting PySpark Job • Spark Web UI • Writing your first PySpark Job Using Jupyter Notebook • Data Ingestion using Sqoop 4.: Playing with Spark RDDs Topics: • Challenges in Existing Computing Methods • Probable Solution & How RDD Solves the Problem • What is RDD, It’s Operations, Transformations & Actions • Data Loading and Saving Through RDDs • Key-Value Pair RDDs • Other Pair RDDs, Two Pair RDDs • RDD Lineage • RDD Persistence • Word Count Program Using RDD Concepts • RDD Partitioning & How it Helps Achieve Parallelization

5.: DataFrames and Spark SQL Topics: • Need for Spark SQL • What is Spark SQL • Spark SQL Architecture • SQL Context in Spark SQL • Schema RDDs • User Defined Functions • Data Frames & Datasets • Interoperating with RDDs • JSON and Parquet File Formats • Loading Data through Different Sources • Spark-Hive Integration 6.: Machine Learning using Spark MLlib Topics: • Why Machine Learning • What is Machine Learning • Where Machine Learning is used • Face Detection: USE CASE • Different Types of Machine Learning Techniques • Introduction to MLlib • Features of MLlib and MLlib Tools

7.: Deep Dive into Spark MLlib Topics: • Supervised Learning: Linear Regression, Logistic Regression, Decision Tree, Random Forest • Unsupervised Learning: K-Means Clustering & How It Works with MLlib • Analysis of US Election Data using MLlib (K-Means) 8.: Understanding Apache Kafka and Apache Flume Topics: • Need for Kafka • What is Kafka • Core Concepts of Kafka • Kafka Architecture • Where is Kafka Used • Understanding the Components of Kafka Cluster • Configuring Kafka Cluster • Kafka Producer and Consumer Java API • Need of Apache Flume • What is Apache Flume • Basic Flume Architecture • Flume Sources • Flume Sinks • Flume Channels • Flume Configuration

9.: Apache Spark Streaming - Processing Multiple Batches Topics: • Drawbacks in Existing Computing Methods • Why Streaming is Necessary • What is Spark Streaming • Spark Streaming Features • Spark Streaming Workflow • How Uber Uses Streaming Data • Streaming Context & DStreams • Transformations on DStreams • Describe Windowed Operators and Why it is Useful • Important Windowed Operators • Slice, Window and ReduceByWindow Operators • Stateful Operators 1.0: Apache Spark Streaming - Data Sources Topics: • Apache Spark Streaming: Data Sources • Streaming Data Source Overview • Apache Flume and Apache Kafka Data Sources • Example: Using a Kafka Direct Data Source 1.1: Spark GraphX (Self-paced) Topics: • Introduction to Spark GraphX • Information about a Graph

• Spark GraphX Algorithm - PageRank, Personalized PageRank, Triangle Count, Shortest

Advanced Artificial Intelligence Course#

1.: Introduction to Text Mining and NLP Topics: • Overview of Text Mining • Need of Text Mining • Natural Language Processing (NLP) in Text Mining • Applications of Text Mining • OS •. Reading, Writing to text and word files • Setting the NLTK Environment • Accessing the NLTK Corpora 2.: Extracting, Cleaning and Preprocessing Text Topics: • Tokenization • Frequency Distribution • Different Types of Tokenizers • Bigrams, Trigrams & Ngrams Discover Learning • Stemming • Lemmatization • Stopwords • POS Tagging • Named Entity Recognition 3.: Analyzing Sentence Structure Topics: • Syntax Trees • Chunking • Chinking • Context Free Grammars (CFG) • Automating Text Paraphrasing 4.: Text Classification-I Topics: • Machine Learning: Brush Up • Bag of Words • Count Vectorizer • Term Frequency (TF) • Inverse Document Frequency (IDF) 5.: Introduction to Deep Learning Topics: • What is Deep Learning? • Curse of Dimensionality

• Use cases of Deep Learning • Human Brain vs. Neural Network • What is Perceptron? • Learning Rate • Epoch • Batch Size • Activation Function • Single Layer Perceptron 6.: Getting Started with TensorFlow 2.0 Topics: • Introduction to TensorFlow 2.x • Installing TensorFlow 2.x • Defining Sequence model layers • Activation Function • Layer Types • Model Compilation • Model Optimizer • Model Loss Function • Model Training • Digit Classification using Simple Neural Network in TensorFlow 2.x • Improving the model • Adding Hidden Layer • Adding Dropout

7.: Convolution Neural Network Topics: • Image Classification Example • What is Convolution • Convolutional Layer Network • Convolutional Layer • Filtering • ReLU Layer • Pooling • Data Flattening • Fully Connected Layer • Predicting a cat or a dog • Saving and Loading a Model • Face Detection using OpenCV 8.: Regional CNN Topics: • Regional-CNN • Selective Search Algorithm • Bounding Box Regression • SVM in RCNN • Pre-trained Model • Model Accuracy • Model Inference Time • Model Size Comparison • Transfer Learning

• mAP • IoU • RCNN – Speed Bottleneck • Fast R-CNN • RoI Pooling • Fast R-CNN – Speed Bottleneck • Faster R-CNN • Feature Pyramid Network (FPN) • Regional Proposal Network (RPN) • Mask R-CNN 9.: Boltzmann Machine & Autoencoder Topics: • What is Boltzmann Machine (BM)? • Identify the issues with BM • Why did RBM come into the picture? • Step-by-step implementation of RBM • Distribution of Boltzmann Machine • Understanding Autoencoders • Architecture of Autoencoders • Brief on types of Autoencoders • Applications of Autoencoders 1.0: Generative Adversarial Network (GAN) Topics: • Which Face is Fake?

• What is Generative Adversarial Network? • How does GAN work? • Step by step Generative Adversarial Network implementation • Types of GAN • Recent Advances: GAN 1.1: Emotion and Gender Detection (Self-paced) Topics: • Which Face is Fake? • Understanding GAN • What is Generative Adversarial Network? • How does GAN work? • Step by step Generative Adversarial Network implementation • Types of GAN • Recent Advances: GAN 1.2: Introduction to RNN and GRU (Self-paced) Topics: • Issues with Feed Forward Network • Recurrent Neural Network (RNN) • Architecture of RNN • Calculation in RNN • Backpropagation and Loss calculation • Applications of RNN • Vanishing Gradient • Exploding Gradient

• Components of GRU • Update gate • Reset gate • Current memory content • Final memory at current time step 1.3: LSTM (Self-paced) Topics: • What is LSTM? • Structure of LSTM • Forget Gate • Input Gate • Output Gate • LSTM architecture • Types of Sequence-Based Model • Sequence Prediction • Sequence Classification • Sequence Generation • Types of LSTM • Vanilla LSTM • Stacked LSTM • CNN LSTM • Bidirectional LSTM • How to increase the efficiency of the model? • Backpropagation through time

1.4: Auto Image Captioning Using CNN LSTM (Self-paced) Topics: • Auto Image Captioning • COCO dataset • Pre-trained model • Inception V3 model • The architecture of Inception V3 • Modify the last layer of a pre-trained model • Freeze model • CNN for image processing • LSTM or text processing 1.5: Developing a Criminal Identification and Detection Application Using OpenCV (Self-paced) Topics: • Why is OpenCV used? • What is OpenCV • Applications • Demo: Build a Criminal Identification and Detection App 1.6: TensorFlow for Deployment (Self-paced) Topics: • Use Case: Amazon’s Virtual Try-Out Room. • Why Deploy models? • Model Deployment: Intuit AI models • Model Deployment: Instagram’s Image Classification Models

• Types of Model Deployment Techniques • TensorFlow Serving • Browser-based Models • What is TensorFlow Serving? • What are Servables? • Demo: Deploy the Model in Practice using TensorFlow Serving • Introduction to Browser based Models • Demo: Deploy a Deep Learning Model in your Browser. 1.7: Text Classification-II (Self-paced) Topics: • Converting text to features and labels • Multinomial Naive Bayes Classifier • Leveraging Confusion Matrix 1.8: In Class Project (Self-paced)

Tableau Certification Training Course#

1.: Data Preparation using Tableau Prep Topics: • Data Visualization • Business Intelligence tools • Introduction to Tableau • Tableau Architecture • Tableau Server Architecture • VizQL • Introduction to Tableau Prep • Tableau Prep Builder User Interface • Data Preparation techniques using Tableau Prep Builder tool 2.: Data Connection with Tableau Desktop Topics: • Features of Tableau Desktop • Connect to data from File and Database • Types of Connections • Joins and Unions Discover Learning

• Data Blending • Tableau Desktop User Interface • Basic project: Create a workbook and publish it 3.: Basic Visual Analytics Topics: • Visual Analytics • Basic Charts: Bar Chart, Line Chart, and Pie Chart • Hierarchies • Data Granularity • Highlighting • Sorting • Filtering • Grouping • Sets 4.: Calculations in Tableau Topics: • Types of Calculations • Built-in Functions (Number, String, Date, Logical and Aggregate) • Operators and Syntax Conventions • Table Calculations • Level Of Detail (LOD) Calculations

5.: Advanced Visual Analytics Topics: • Parameters • Tool tips • Trend lines • Reference lines • Forecasting • Clustering 6.: Level Of Detail (LOD) Expressions In Tableau Topics: • Use Case I - Count Customer by Order • Use Case II - Profit per Business Day • Use Case III - Comparative Sales • Use Case IV - Profit Vs Target • Use Case V - Finding the second order date. • Use Case VI - Cohort Analysis 7.: Geographic Visualizations in Tableau Topics: • Introduction to Geographic Visualizations • Manually assigning Geographical Locations • Types of Maps • Spatial Files • Custom Geocoding • Polygon Maps

• Background Images 8.: Advanced Charts in Tableau Topics: • Box and Whisker’s Plot • Bullet Chart • Bar in Bar Chart • Gantt Chart • Waterfall Chart • Pareto Chart • Control Chart • Funnel Chart • Bump Chart • Step and Jump Lines • Word Cloud • Donut Chart 9.: Dashboards and Stories Topics: • Introduction to Dashboards • The Dashboard Interface • Dashboard Objects • Building a Dashboard • Dashboard Layouts and Formatting • Interactive Dashboards with actions • Designing Dashboards for devices

1.0: Get Industry Ready Topics: • Tableau Tips and Tricks • Choosing the right type of Chart • Format Style • Data Visualization best practices • Prepare for Tableau Interview 1.1: Exploring Tableau Online Topics: • Publishing Workbooks to Tableau Online • Interacting with Content on Tableau Online • Data Management through Tableau Catalog • AI-Powered features in Tableau Online (Ask Data and Explain Data) • Understand Scheduling • Managing Permissions on Tableau Online • Data Security with Filters in Tableau Online

Price#

  • Cost: : ₹89,000

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