DATA ANALYTICS COURSE TRAINING IN HYDERABAD

About LearnAi.co.in

LearnAI.co.in, a leading training institute, offers a comprehensive Data Analytics course suitable for beginners, intermediates, and experts. Our beginner-friendly curriculum provides a solid foundation in data analysis, covering fundamental concepts and tools. Intermediate learners benefit from in-depth training, exploring advanced statistical techniques and data visualization. For experts, we offer specialized modules focusing on cutting-edge technologies and real-world application.

Our courses are designed to ensure an enjoyable and accessible learning experience for all levels, with a practical approach that equips students with the skills to excel in the field of data analytics. Join us at LearnAI.co.in and embark on a journey that caters to your expertise, helping you achieve your data analytics goals with ease.

Upcoming Batches

Course	Branch	Training Type	Week	Day
Data Analytics	Dilsukhnagar	Hybrid	1st	Wednesday
Data Analytics	Dilsukhnagar	Hybrid	2nd	Monday
Data Analytics	Dilsukhnagar	Hybrid	3rd	Wednesday
Data Analytics	Dilsukhnagar	Hybrid	4th	Monday

What is Data Analytics?

Data analytics is the process of examining and interpreting data to uncover meaningful insights and trends. It helps businesses make informed decisions and improve their operations by turning raw data into actionable knowledge.

Why to learnData Analytics?

Learning data analytics is a strategic career move because it equips you with the skills to transform data into valuable insights, making you an asset to any industry. In a data-driven world, professionals who understand data analytics are in high demand, opening up numerous career opportunities and ensuring long-term growth prospects.

Who can Pursue Data Analytics?

Any Degree pursuing or Graduated,
 Bachelor’s, Master’s, PhD, and
 Anyone interested in course

Enroll to Course Now

COURSE CURRICULUM

Python

Introduction
 Installation
 Fundamental of Python
 Variables
 Comments
 Print Statement
 Operators
 Mutable Data Types
Data Types
Special Data Types
Conditions
Loops
Functions
Break, Continue and Pass Statements
String Object and working
List
Tuple
Set
Dictionaries
Map
Reduce
Filter
Classes
Objects
Inheritance
Multiple Inheritance
Modules
Error Handling

Data Visualization

Matplotlib
 Seaborn
 Plotly
 Cuflinks
 Bokesh

Libraries

Numpy
Pandas
Random
Math
Scipy
sklearn
Keras
Tensorflow
OpenCV
NLTK
Spacy
Lot more…

Tableau

Introduction to Data Visualization
Introduction to Tableau
Basics charts and dashboards
Special Char Types
Dashboard design and principles
Connections with servers
Local file access
Hands on experience with worksheet

Web Scraping

 Url
 Beautiful Soup

Data base

 SQL
 MongoDB

Statistics

Basics of Statistics
Descriptive Statistics
Inferential Statistics
Qualitative Analysis
Quantitative Analysis
Hypothesis Testing
Data Distribution
Probability Distribution
Normal Distribution
Poison Distribution
Outlier Detection
Other Statistical Fundamentals

Machine Learning

Supervised Learning
Unsupervised Learning
Semi Supervised Learning
Reinforcement Learning

Supervised Learning

 Regression Models
Classification Models

Regression Models

Introduction to Regression Models
Linear Models
Linear Regression
Multiple Regression
Ordinary Least Square Method
Non-Linear Models
Support Vector Regressor
Random Forest Regressor
Decision Tree

Evaluation Metrics for Regression Models

R-Square
 Adjusted R Square
 Mean Square Error
Root Mean Square Error
Mean Absolute Error

Classification Models

Introduction to Classification Models
 Logistics Regression
 Naïve Bayes
 Support Vector Classifier
 K-NN Classifier
 Decision Tree Classifier
 Random Forest Classifier

Evaluation Metrics for Classification Models

Accuracy
Precision
Recall
F1 Score
ROC Curve

Unsupervised Learning

Introduction to Unsupervised Learning

Clustering Models

 Introduction to Clustering Models
 K- Mean Clustering
 Hierarchical Clustering
High Dimensional Clustering

Dimension Reduction

Principal Component Analysis (PCA) Reinforcement Learning

Reinforcement Learning

Introduction to Reinforcement Learning

Featurization, Model Selection & Tuning

Feature Extraction
Model Defects & Evaluation Metrics
Model Selection and Tuning
Comparison of machine learning models

Neural Networks

Introduction to Deep Learning
Fundamental of Neural Networks
TensorFlow and Keras
Artificial Neural Networks (ANN)
Convolution Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Evaluation of Deep Learning
Neural Networks Basics
Gradient Descent
Introduction to Perceptron & Neural Networks
Batch Normalization
Activation and Loss Functions
Hyper parameter tuning
SoftMax
Deep Neural Networks
Weights initialization

Image Pre-processing

Computer Vision
Open CV
Noise Detection
Noise Reduce
Low Pass Filters
Forward Propagation
Backward Propagation
Pooling & Padding

Natural Language Processing

Introduction to NLP
 Corpus
 Natural Language Understanding (NLU)
 Natural Language Generator (NLG)
Tokenization (Word, Sentence, Blank, RegEx)
Frequency Distribution
Filtering
Stemming
Lemmatization
Stop Words
Regular Expressions
 POS Tagging
 Syntax Tree
 Chunking
 Lemmas
 Hypernyms
 Hyponyms
 Synonyms
 Antonyms
 Distance Between words
Wordnet
 Name Entity Recognition
 Bag of words or Document Matrix
 Count Vectorization
 Term Frequency
 Inverse Document Frequency
 Sentimental Analysis

Job Roles

Data Analyst
Business Intelligence Analyst
Data Scientist
Data Engineer
Business Analyst
Market Research Analyst
Healthcare Data Analyst
Financial Analyst
Marketing Analyst
Supply Chain Analyst
HR Analyst
Fraud Analyst
Environmental Data Analyst
Sports Data Analyst
E-commerce Analyst

Components of Data Analytics

Data Sources
Data Storage
Data Cleaning Tools
Data Analysis Software
Data Visualization Tools
Machine Learning Libraries
Business Intelligence (BI) Tools
Statistical Techniques
Programming Languages
Data Governance and Security
Data Analysts and Data Scientists
Data Engineering Team
Domain Knowledge
Cloud Computing Services
Data Governance Tools
Collaboration Tools
Data Analytics Models and Algorithms
Data Quality Assessment Tools
Data Reporting and Documentation
Continuous Learning and Training