ADVANCED BIG DATA SCIENCE TRAINING IN KOCHI

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4.8/5
1245 Learners

Duration : 3 Months – Weekends 3 Hours on Saturday and Sundays

Real-Time Projects, Assignments, scenarios are part of this course

Data Sets, installations, Interview Preparations, Repeat the session until 6 months are all attractions of this particular course

Overview

This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode, etc., and eventually covers all aspects of an analytics (or) data science career from analyzing and preparing raw data to visualize your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic to Advance techniques used by real-world industry data scientists.

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INTRO VIDEO

Why Radical Technologies

  • Highly practical oriented training
  • 25000+ Man-hours of Real-time projects & scenarios
  • 10 to 20+ year Experienced corporate trainers With Real Time Experience.
  • Building up professionals by highly experienced professionals
  • 100 % quality assurance in training .
  • 10000+ Placement Records and 180+ MNC’s and Consultancies Tie up

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Course Curriculum

Course description

Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce, and Graph datasets.

Course Content

  • What is Data Science?
  • Why Python for data science?
  • Relevance in industry and need of the hour
  • How leading companies are harnessing the power of Data Science with Python?
  • Different phases of a typical Analytics/Data Science projects and role of python
  • Anaconda vs. Python
  • Overview of Python- Starting with Python
  • Introduction to the installation of Python
  • Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython, etc…)
  • Understand Jupyter notebook & Customize Settings
  • Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
  • Installing & loading Packages & Name Spaces
  • Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions
  • Variable & Value Labels –  Date & Time Values
  • Basic Operations – Mathematical – string – date
  • Reading and writing data
  • Simple plotting
  • Control flow & conditional statements
  • Debugging & Code profiling
  • How to create classes and modules and how to call them?
  • Scientific distributions used in python for Data Science – Numpy, scify, pandas, scikitlearn, statmodels, nltk etc
  • Importing Data from various sources (CSV, txt, excel, access, etc)
  • Database Input (Connecting to the database)
  • Viewing Data objects – subsetting, methods
  • Exporting Data to various formats
  • Important python modules: Pandas, beautifulsoup
  • Cleansing Data with Python
  • Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
  • Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
  • Python Built-in Functions (Text, numeric, date, utility functions)
  • Python User Defined Functions
  • Stripping out extraneous information
  • Normalizing data
  • Formatting data
  • Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime, etc)
  • Introduction exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
  • Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)
  • Basic Statistics – Measures of Central Tendencies and Variance
  • Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
  • Inferential Statistics -Sampling – Concept of Hypothesis Testing
  • Statistical Methods – Z/t-tests (One sample, independent, paired), Anova, Correlation, and Chi-square
  • Important modules for statistical methods: Numpy, Scipy, Pandas
  • Introduction to Machine Learning & Predictive Modeling
  • Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
  • Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
  • Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
  • Overfitting (Bias-Variance Tradeoff) & Performance Metrics
  • Feature engineering & dimension reduction
  • Concept of optimization & cost function
  • Concept of the gradient descent algorithm
  • Concept of Cross-validation(Bootstrapping, K-Fold validation, etc)
  • Model performance metrics (R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics)
  • Linear & Logistic Regression
  • Segmentation – Cluster Analysis (K-Means)
  • Decision Trees (CART/CD 5.0)
  • Ensemble Learning (Random Forest, Bagging & boosting)
  • Artificial Neural Networks(ANN)
  • Support Vector Machines(SVM)
  • Other Techniques (KNN, Naïve Bayes, PCA)
  • Introduction to Text Mining using NLTK
  • Introduction to Time Series Forecasting (Decomposition & ARIMA)
  • Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk, etc)
  • Fine-tuning the models using Hyperparameters, grid search, piping, etc.
  • Applying different algorithms to solve the business problems and bench mark the results
  • Introduction and Relevance
  • Uses of Big Data analytics in various industries like Telecom, E-commerce, Finance, and Insurance, etc.
  • Problems with Traditional Large-Scale Systems
  • Motivation for Hadoop
  • Different types of projects by Apache
  • Role of projects in the Hadoop Ecosystem
  • Key technology foundations required for Big Data
  • Limitations and Solutions of existing Data Analytics Architecture
  • Comparison of traditional data management systems with Big Data management systems
  • Evaluate key framework requirements for Big Data analytics
  • Hadoop Ecosystem & Hadoop 2.x core components
  • Explain the relevance of real-time data
  • Explain how to use Big Data and real-time data as a Business planning tool
  • Hadoop Master-Slave Architecture
  • The Hadoop Distributed File System – Concept of data storage
  • Explain different types of cluster setups(Fully distributed/Pseudo etc)
  • Hadoop cluster set up – Installation
  • Hadoop 2.x Cluster Architecture
  • A Typical enterprise cluster – Hadoop Cluster Modes
  • Understanding cluster management tools like Cloudera manager/Apache ambari
  • HDFS Overview & Data storage in HDFS
  • Get the data into Hadoop from local machine(Data Loading Techniques) – vice versa
  • Map Reduce Overview (Traditional way Vs. MapReduce way)
  • Concept of Mapper & Reducer
  • Understanding MapReduce program Framework
  • Develop a MapReduce Program using Java (Basic)
  • Develop a MapReduce program with streaming API) (Basic)
  • Integrating Hadoop into an Existing Enterprise
  • Loading Data from an RDBMS into HDFS by Using Sqoop
  • Managing Real-Time Data Using Flume
  • Accessing HDFS from Legacy Systems
  • Introduction to Data Analysis Tools
  • Apache PIG – MapReduce Vs Pig, Pig Use Cases
  • PIG’s Data Model
  • PIG Streaming
  • Pig Latin Program & Execution
  • Pig Latin: Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Pig UDF
  • Writing JAVA UDF’s
  • Embedded PIG in JAVA
  • PIG Macros
  • Parameter Substitution
  • Use Pig to automate the design and implementation of MapReduce applications
  • Use Pig to apply structure to unstructured Big Data
  • Apache Hive – Hive Vs. PIG – Hive Use Cases
  • Discuss the Hive data storage principle
  • Explain the File formats and Records formats supported by the Hive environment
  • Perform operations with data in Hive
  • Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts
  • Hive Script, Hive UDF
  • Hive Persistence formats
  • Loading data in Hive – Methods
  • Serialization & Deserialization
  • Handling Text data using Hive
  • Integrating external BI tools with Hadoop Hive
  • Impala & Architecture
  • How Impala executes Queries and their importance
  • Hive vs. PIG vs. Impala
  • Extending Impala with User Defined functions
  • NoSQL database – Hbase
  • Introduction Oozie
  • Introduction to Apache Spark
  • Streaming Data Vs. In-Memory Data
  • Map Reduce Vs. Spark
  • Modes of Spark
  • Spark Installation Demo
  • Overview of Spark on a cluster
  • Spark Standalone Cluster
  • Invoking Spark Shell
  • Creating the Spark Context
  • Loading a File in Shell
  • Performing Some Basic Operations on Files in Spark Shell
  • Caching Overview
  • Distributed Persistence
  • Spark Streaming Overview(Example: Streaming Word Count)
  • Analyze Hive and Spark SQL Architecture
  • Analyze Spark SQL
  • The context in Spark SQL
  • Implement a sample example for Spark SQL
  • Integrating hive and Spark SQL
  • Support for JSON and Parquet File Formats Implement Data Visualization in Spark
  • Loading of Data
  • Hive Queries through Spark
  • Performance Tuning Tips in Spark
  • Shared Variables: Broadcast Variables & Accumulators
  • Extract and analyze the data from Twitter using Spark streaming
  • Comparison of Spark and Storm – Overview
  • Overview of GraphX module in spark
  • Creating graphs with GraphX
  • Understand Machine learning framework
  • Implement some of the ML algorithms using Spark MLLib
  • Consolidate all the learnings
  • Working on Big Data Project by integrating various key components

Training Options

Live Online Training

  • Highly practical oriented training
  • Installation of Software On your System
  • 24/7 Email and Phone Support
  • 100% Placement Assistance until you get placed
  • Global Certification Preparation
  • Trainer Student Interactive Portal
  • Assignments and Projects Guided by Mentors
  • And Many More Features

Course completion certificate and Global Certifications are part of our all Master Program

Live Classroom Training

  • Weekend / Weekdays / Morning / Evening Batches
  • 80:20 Practical and Theory Ratio
  • Real-life Case Studies
  • Easy Coverup if you missed any sessions
  • PSI | Kryterion | Redhat Test Centers
  • Life Time Video Classroom Access ( coming soon )
  • Resume Preparations and Mock Interviews
  • And Many More Features

Course completion certificate and Global Certifications are part of our all Master Program

Exam & Certification

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ADVANCED BIG DATA SCIENCE TRAINING IN KOCHI

Excellent quality content! It's a great introductory course that really gets you interested in Data Science. I would highly recommend it to anyone curious in learning about what Data Science is about.

Course Provider: Organization

Course Provider Name: Radical Technologies

Course Provider URL: https://radicals.in/

Editor's Rating:
5