Certification program in Data Analytics (Customized Track)
About Course
This is the program where you will be trained with fundamentals of data analytics, Machine learning, Data visualization and Python. The entire content provides you with the comprehensive theory and practical knowledge driven by highly qualified professionals from the Data Science spectrum.
With the help of our carefully curated training programs, you will be professionally groomed and have your skill sets enhanced. This will be done through hands-on projects, practical assignments, case studies, communication skills, resume building sessions, mock tests, assessments and mock interviews.
Features of program
- Live 1.5 Hours/day training sessions by GapReduce certified trainer
- Case studies and project on data analytics
- Module wise assessments
- Coding assessments
- Resume building sessions
- One to one mock interviews
- MNC specific interview question bank + practice test
- Job application support
- Placement support from GapReduce placement team
Program Modes
Certification program in Data Analytics is offered in two modes mentioned below:
Interview Guaranteed
In GapReduce Interview Guaranteed programs, we offer guarantee for interview in respective domains covered by program. During this time, even after completion of course, learner will be given complete support to update resume, prepare profiles like linkedIn for job opportunities. Learner will be given regular updated about job opportunities and complete assistance in applying for the same.
Job Guaranteed
In GapReduce Job Guaranteed programs, we offer guarantee till the placement of learner. Learner will start receiving job updates after 70 % completion of course and this support will be given for a period of one year after completion of course. During this time, learners will be given regular updates, interview preparation question banks, mock interview and resume update sessions.
What Will You Learn?
- Fundamentals of probability
- Statistical methods for data analytics
- Python for Data Analytics
- Machine learning algorithms
- Data Visualization
- Handling Big Data using Apache Hadoop
Course Content
Induction Session
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Induction Session
01:09:44
Module 1: Fundamentals of Probability and Statistics
-
Pre Enrollment Assessment
-
Introduction to Data Analytics
01:07:04 -
Introduction to Statistics
01:16:01 -
Methods of center measurement
01:28:05 -
Methods of variability measurement
01:25:01 -
Introduction to Statistical Inference
01:20:31 -
Sample and Population
01:15:43 -
Central limit theorem
01:28:38 -
Normal Distribution and Hypothesis testing
01:16:43 -
Revisiting Module 1 Session 1
02:07:39 -
Revisiting Module 1 Session 2
02:09:20 -
Revisiting Module 1 Session 3
01:26:27 -
Revisiting Module 1 Session 4
01:43:24 -
Assessment – Module 1
-
Module 1 Assessment Discussion
01:19:37
Module 2: Statistics for Decision Making
-
Introduction to Hypothesis Testing
01:19:37 -
More about Hypothesis Testing
01:30:02 -
Introduction to T Test
01:33:15 -
T-test Deep Dive
01:26:30 -
T-Test and Z-Test
01:24:13 -
ANOVA One Way
01:23:10 -
Post ANOVA
01:02:21 -
ANOVA Two Way
01:24:14 -
ANOVA Two Way – Without Replication
01:17:31 -
Linear Regression in Excel
01:24:39
Module 3: Introduction to Data Analytics
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Introduction to Analytics
01:24:21 -
Categorization of Data
01:23:35 -
Deep dive into categorization of data
01:28:15 -
Types of Data Analytics
01:30:32
Module 4: Python for Data Analytics
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Introduction to Programming
01:27:19 -
Data types in Python
01:23:14 -
Strings and Lists in Python
01:16:54 -
Tuples and Dataframes in Python
01:58:08 -
Type Casting and Operators
01:32:49 -
Bitwise Operators
01:20:30 -
Operator Associativity and Precedence
01:13:02 -
Decision Making Statements
01:22:23 -
Iterative Statements and Introduction to Functions
01:23:19 -
More about Functions
01:27:14 -
Practice Problems on Python Fundamentals Part-1
01:26:33 -
Practice Problems on Python Fundamentals Part-2
01:14:38 -
Practice Problems on Python Fundamentals Part-3
01:17:59 -
Introduction to Classes and Objects
01:21:33 -
Practice Problem – Binary Search
01:29:28 -
Inheritance and Polymorphism
01:26:16 -
General Discussion on Capstone Project
01:17:27 -
DataFrames in Pandas
01:28:40 -
DataFrame Operations
01:29:33 -
Introduction to Weka For Machine Learning
01:20:56 -
Aggregation Functions and Handling Missing Values
01:35:40 -
Handling Missing Values Activity
56:19 -
Numpy and Exception Handling
01:48:09 -
Statistical Approaches in Python
01:36:49
Module 5: Introduction to Machine Learning
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Introduction to Supervised Machine Learning
01:21:44 -
Introduction to Classification
01:21:24 -
Naive Bayes Algorithm
01:24:43 -
Implementation of Naive Bayes for Numeric Attribute Only Dataset
01:53:48 -
Implementation of Naive Bayes with Categorical Data Encoding
01:09:36 -
Introduction to SVM and Decision Trees
01:59:22 -
Decision Trees and Entropy
02:04:12 -
Information Gain and Deploying Decision Tree
01:22:00 -
KNN for Classification
01:25:31 -
Random Forest
01:22:20 -
Ensemble Models for Classification
01:31:48
Module 6: Regression and Clustering
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Linear Regression
01:25:09 -
Executing Python Programs in Ubuntu
01:15:26 -
Classification Evaluation Metrics and Linear Regression in Python
01:24:46 -
Logistic Regression and Evaluation Metrics in Regression
01:32:51 -
R2 Value and Implementation
01:35:11 -
Introduction to Clustering
01:28:48 -
KMeans Algorithms and Elbow Method
01:21:44 -
Hierarchical Clustering and Outlier Detection
01:33:38
Module 7: Deep dive into data pre processing and performance efficiency
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Outlier Detection and Removal
01:25:44 -
Feature Scaling
01:10:56 -
Principal Component Analysis
01:21:55 -
PCA Revisit
27:35 -
Feature Selection and Ranking
01:10:17 -
Implementation of Feature Scaling and Ranking Algorithms
01:27:50 -
Implementation of Exhaustive Feature Selection and PCA
01:28:35 -
Techniques to Prevent Overfitting
01:26:52 -
Data Visualization using Matplotlib and Seaborn
01:24:21 -
L1 and L2 Regularization and SQL Installation
01:22:03
Module 8: Structured Query Language (SQL)
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MySQL Installation in Ubuntu
01:22:32 -
Data Types in SQL
01:28:35 -
Constraints in Create command
01:28:24 -
DDL Commands
01:21:13 -
DQL Command – Select
01:45:11 -
Nested Queries and Functions in MySQL
01:25:23 -
Joins in SQL
01:32:33 -
Restarting SQL Sessions
01:16:55 -
Operators and Clauses with SELECT and INSERT Statements
01:01:28 -
Conditional Join and LIKE Clause
58:57 -
Constraints and Keys
01:32:36
Module 9: Big Data Analytics
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Fundamentals of Computer Architecture and Distributed Computing
01:27:14 -
Configuring Apache Hadoop
02:08:06 -
Big Data and Apache Hadoop
01:59:57 -
Insight to Hadoop Configuration
01:36:48 -
Apache Hadoop Architecture
01:06:17 -
HDFS Operations
01:34:35 -
Hadoop Configuraton Files Deep Dive
01:16:39 -
Introduction to Apache Hadoop Ecosystem
01:11:26 -
Apache Pig Grunt Shell
01:17:57 -
Apache Pig: Operators
01:21:32 -
More into Operators and Introduction to HBase
01:27:57 -
HBase and HDFS
01:17:12 -
HBase Architecture and Statements
01:15:42
Module 10: Tableau
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Getting started with Tableau
01:25:11 -
Sheets and Dashboard in Tableau
01:28:48 -
Joins in Tableau
01:03:54 -
Group, Set, Calculated Field and Parameters
01:09:55
Interviews and Placement
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Placement Readiness Program
10:26 -
Mock Interview Feedback and Tips
41:04 -
Placement Preparation – Module 1 and 2 Discussion
01:36:18 -
Placement Preparation – Module 1 and 2 Discussion
01:36:18
Capstone Project
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Project Dataset Discussion
01:37:18 -
Problem Statement and Project Management
01:27:35 -
Electricity Demand Forecast Project Discussion
59:41 -
Kaggle Projects Discussion
52:58 -
Kaggle Projects Doubts Discussion
01:23:00 -
Electricity Demand Forecasting Project Implementation Part1
01:09:41 -
Electricity Demand Forecasting Project Implementation Part2
01:07:08 -
Electricity Demand Forecasting Project Implementation Part3
01:29:44 -
Electricity Demand Forecasting Project Implementation Part4
01:22:20 -
Project Report Discussion and Kaggle Submissions Analysis
01:26:29
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