Data Analyst Course

Name: Quality Thought Software Training Institute
Brand: Quality Thought
Rating: 4.5 (1972 reviews)

Job Oriented Intensive Program

Want to be a data analyst? Our 3-month Data Analyst course at Quality Thoughts is the ticket! Learn the basics of Python, stats, SQL, Excel, Advance Excel, Power BI, and more. Get ahead with your skills and land your dream job. Enroll yourself in the Data Analyst Course right away!

Call : +91 9963486280

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Data Science Training Course Upcoming Batches

Date:

26th March

Time

9:00 AM TO 06:00 PM

Program Duration:

90 Days

Learning Format:

Training

Course Curriculum

Date:

26th March

Time

11:00 AM TO 06:00 PM

Program Duration:

150 Days

Learning Format:

JOIP/I&I

Course Curriculum

India’s #1 Software Training Institute

Key Features That Make the Difference

Build a Complete Set of Data Analyst Fundamentals:

Core concepts in data analysis

Work on Real-World Projects:

Give ways to apply theoretical skills to actual projects

Group Learning Environment:

Opportunity to learn along with peers

Industry-based Case Study:

Real-time challenges faced and find out how to solve them.

Comprehensive Training

2 Hours Training
Industry Curriculum
Learn from the Practitioner
Experimentation Learning
Professional Certification
Education Loan
Experience Industry Faculty
Dedicated Placement Team

Key Skills for Your Success

Skills Covered

Exclusive Training

Any Graduate
1-2 Hours Training
60 Days
Real Time Faculty
Lab Assistant
Maximum 3.5 Months Training
2 Mock Interviews Monthly
15 Days Once Mock Tests
Support With Resume Preparation
Reference Of Job Openings
Training Completion Certificate
Course Fee: 35,000

PLACEMENTS ORIENTED INTENSIVE PROGRAM (POIP)

Any Graduate
6-8 Hours Training
3-4 Months
Soft Skills Training
Aptitude Training
Real Time Faculty Team (Group Of Working Professionals)
Training-3-6 Months,Every Week 1 Day Project Support Until Student Gets A Job
Continuous Sprints Support
Monthly 2 Mock Interviews Until They Get A Job
Get Access To 10+ Live Resumes
Get Access To QT Master LMS
Up To 3 Live Projects Training
Training Completion Certificate
Dedicated Placement Officer
Course Fee: 35,000 + 50,000

COMPANY ORIENTED INTENSIVE PROGRAM (COIP)

Any Graduate/Any Qualifying Year/Gap
Internship With Real time Environment
6-8 Hours Training, 3-9 Months
Soft Skills Training
Aptitude Training
Project Environment From Day 1 Until The Student Gets Placed
Project Experience Until Student Gets A Job
Every Week Mock Interview
Get Access To 50+ Live Resumes.
Get Access To QT Master LMS
Up To 3 Live Projects Training
Internship Certificate From Client
Dedicated Placement Officer
Course Fee: 90,000 + 30,000

Pre Requisites

Basic statistics and probability understanding
Familiarity with data cleaning and manipulation
Proficiency in Microsoft Excel
Understanding of programming basics (e.g., Python or SQL)
Strong problem-solving and analytical skills
Curiosity and passion for data-driven insights

Eligibility

01: Introduction

Introduction to Jupyter Notebook

Getting Started with Data Science

Unix Introduction

02: PYTHON

Python Basics
Python Introduction
Python Data Structure: Lists and Arrays
Python: Conditions and Branching
Python: Functions and Methods
Python: Objects and Classes
Practice Questions in Python
Introduction to NumPy
Linear Algebra in NumPy
Seaborn, Matplotlib
Project 1: Satellite Image Data Analysis using NumPy
Introduction to Pandas

03: PROBABILITY AND STATISTICS

Introduction to Probability

Probability Distributions

Describing Distributions

Probability Distribution with Multiple Variables

Population and Sample

Point Estimate

Confidence Interval

Hypothesis Testing

A/B Testing

04: CALCULUS

Derivatives

Optimization

Gradients

Gradient Decent

Optimization in Neural Networks

Newton Methods

05: LINEAR ALGEBRA

System of Linear Equations

Elimination Method

Row and Row Reduced Echelon form

Vector Algebra

Linear Transformation

Determinants

Eigen Values of Eigen Vectors

06: DATA STRUCTURE ALGORITHMS

Array

String

Linked List

Searching Algorithm

Sorting Algorithm

Divide and Conquer Acqu

Stack

Queue

Tree Data Structures

Graph Data Structures

Dynamic Program

07: DATA SCIENCE AND METHODOLOGY

Data Acquisition

Data Wrangling

Data Statistical Analysis, Grouping and Correlation

Model Development

Model Evaluation and Refinement

Getting started in scikit-learn with the famous iris dataset

Training a Machine Learning Model with scikit-learn

Comparing Machine Learning Models in scikit-learn

Data Science Pipeline: Pandas, Seaborn, and scikit-learn

Cross-Validation for Parameter Tuning, Model Selection, and Feature Selection

Efficiently Searching for Optimal Tuning Parameters

Evaluating a Classification Model: Confusion Matrix and ROC

08: DATA VISUALIZATION

Basic Plotting for Data Visualisation

Data Manipulation for Visualisation

1D Data Analysis: Histograms, Boxplots, and Violin Plots

Project 2: Visualization of world GDP and carbon dioxide emission

Project 3: Using Folium Library for Geographic Overlays

09: Power - BI

Introduction to Power-Bi

Data Extraction Process

Data Transformations

Data Modeling and DAX

Data Visualization with Analytics

Power-Bi, Q&A & Data Insights

10: Machine Learning

Simple Linear Regression

Multiple Linear Regression

Non-Linear Regression

Regression Methods

Ridge Regression and Lasso Regression

Linear Regression and Decision Tree Regression

Random Forest Regression

Logistic Regression

Decision Tree Classification

Random Forest Classification

Boosting Algorithms

Bagging

K- Nearest Neighbours Classification

Naive Bayes Classification

K-Means Clustering

Hierarchical Clustering

K-Means and Hierarchical Clustering on the same dataset

Density-Based Spatial Clustering of Applications with Noise (DB-SCAN) Support Vector Machines & Regression

Principal Component Analysis (PCA)

Applying Principal Component Analysis on Handwritten Digits Dataset

Market Basket Analysis

11: ALGORITHM DESIGN AND ANALYSIS

Evaluate the speed, runtime and memory dependencies of algorithmic models Parallel computing systems such as SISD (Single Instruction Single Data Stream), SIMD (Single Instruction Multiple Data Streams), MISD (MultipleInstructions Single Data Stream), MIMD (Multiple Instructions Multiple DataStreams)

How to use coding tools

Create, review and execute unit test cases Corrective and Preventive actions for problems and defects can improve future designs

Measure and Optimize performance of algorithm

Deployment of the Models

Why Choose Quality Thought

100% Success Rates in the Placement for Skilled People

Flexible learning options: Learn online, offline, or a combination to suit your lifestyle and schedule
Millions of learners: Be part of a community of millions of people turning their dreams into reality through Quality Thought programs.
New age courses: Engage with the best curriculum of Quality Thought and take up the latest skills and knowledge in the most in-demand fields today.
New teaching methods: Quality Thought comes through with creative teaching methods that create interactive learning experiences, that would help you understand more than memorize.
Support for a career: Resume writing assistance, interview preparation, and job placement to get you started on your successful career.
Exploits the positive experiences of alumni: uch includes statements or success stories of previous graduates to prove the worth of Quality Thought programs.

A gate way to your🤔 Bright Future in the IT industry

Connect with us for Life-changing opportunities

Course completion: Complete all lessons.
Submission of assignment: Submit all projects and assignments on time.
Certificate receipt: Obtain the certificate within one week.
Authenticity: Verify the authenticity of the certificate.

What can be understood as Data Analytics, and why is this problem relevant?

Data analytics is comprised of the process of working with large datasets to identify common patterns. Qualitative tools assist businesses in making the right decisions and therefore improving their efficiencies and competitiveness.

Do I need a programming background or mathematics to learn data analytics?

It is not necessary. Most classes in data analytics are entry-level, and as a result, users will learn all the tools they require from day one.

What is the most frequently demanded by users and software for data analysts?

They include; Python, SQL, R, tableaus, and Excel. These are the tools for cleaning, manipulating, and analyzing data as well as data visualization.

What are some career paths that would be available upon completion of a data analytics course?

Thus, some of the job openings, created by the current AI development are data analyst, data scientist, business intelligence analyst, market research analyst, and the rest.

How much will a data analyst normally get?

Wages depend on the experience and location of the employee. Nevertheless, data analysts are often provided with rather generous salary jobs.

Data Analyst Course

Job Oriented Intensive Program

Call : +91 9963486280

Start Your Journey Today

Data Science Training Course Upcoming Batches

Program Duration:

Key Features That Make the Difference