Data Science

Data Science Training in Kphb Hyderabad

Data Science with Python Training Overview

Data Science AI Deep Machine Learning training in Hyderabad. It is developed with data science tool and which is used to simplify and easily access the data and store the data easily. By R Programming language we can easily manipulate the data, also it can help in the analysis of Data, we can create the wonderful visualization and helps to access the high-quality content. This data science course in Kukatpally with Python Training provides you to learn data manipulation and cleaning of data using python.

Objectives of the Data Science Course in Kukatpally

Complete basics of Data Science Course in Kukatpally
The concepts of BigData and able to work in Data mining.
Understand the usage and how to use the tools like a tableau, map-reduce…

Pre-requisites of the data science course in kukatpally

IT experienced Professional who are interested to build their career in development/ data scientist.
Any B.E/ B.Tech/ BSC/ MCA/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.
Fresh Graduates.

Who can attend this data science with python training in hyderabad

The course can learn by any IT professional having basic knowledge of:

Mathematics
Statistics
Any Programming Language

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Course Details:

Note: This course is for the students with basic knowledge on any other programming language.

Duration: 10 weeks

Time : 8am or 7pm

Mode : class room

Course Fee:Contact Us

Trainer :

name: RAJESH

Qualification : B.Tech, IIIT Hyderabad

Experience : Software professional with 10 yrs

Courses :Data Science PYTHON

Course Content

SNO	TOPIC	SUB TOPIC		duration
0	Machine Learning	introduction
1	Introduction to Statistics	Mean, Mode Variance covariance Standard deviation Correlation Coefficient Statistical Inference concepts: Hypothesis Testing p-values Types of Data Distributions T-test, Z-test, Chi-Square, ANOVA test Correlation, Covariance Central Limit Theorem & Probability		1 week
2	Python	data structures drawing graphs Numpy Pandas sklearn	Lists,Tuples,sets,dictionaries Scatter, plot, Pie and Bar arrays,matrix,statistics series,frames,read csv/excel Data sets Apply Data Frame functions	2 week
	Data Analysis	data processing Feature scaling TensorFlow	read data from csv/excel sheet Extract rows/ columns update df with new cols identify NAN values identify invalid data predict the value and replace Introduction to TensorFlow Introduction to Tensor with Tensorflow Linear Regression Using Tensorflow	1 week
3	Regression	Linear regression Multiple regression Polynomial Regression		1 week

4	Classification	logistic regression Naïve Bayes Classifier K nearest neighbors Support Vector Machines (SVM) Decision Tree random forest Time Series Analysis – ARIMA, Auto ARIMA		1 week
5	Clustering	K means clustering
6	Model Selection	Principal Compenent Analysis (PCA) Recommendation Systems
7	Natural language processing NLP	Text Preprocessing Word tokenize Stop words Stemming Part of speech tagging Chunking llemmatizing and corpora Word net Topic Modeling Sentiment Analysis Vector Space Models: Word2Vec, GloVe		1 week
8	Deep Learning	Neural networks Single Layer & Multi-Layer Perceptron Forward Propagation Types of Activation functions Bias & Weights Optimization Techniques: RMSprop, Adam, Adagrad Architectures: Convolutional Neural Networks, CNN Recurrent Neural Networks, RNN & LSTM		1 week

Statistics:

Statistical Inference concepts: Hypothesis Testing, p-values, Types of Data Distributions
T-test, Z-test, Chi-Square, ANOVA test
Correlation, Covariance
Central Limit Theorem & Probability

Supervised Models:

Linear & Logistic Regression
Decision Trees
Ensemble Models
1. Bagging - Random Forest
2. Boosting – Adaboost, Gradient Boost, XGBoost, LightGBM
Support Vector Machine
Naïve Bayes Classifier
K Nearest Neighbor (KNN)
Time Series Analysis – ARIMA, Auto ARIMA

Unsupervised Models:

Clustering
Dimensionality Reduction: Principal Component Analysis
Recommendation Systems

Natural Language Processing – NLP:

Text Preprocessing- CountVectorizer, TfIdfVectorizer, Stop word Removal…
Topic Modeling
Sentiment Analysis
Information Retrieval
Vector Space Models: Word2Vec, GloVe

Deep Learning:

Neural Networks: Single Layer & Multi-Layer Perceptron
Forward Propagation
1. Types of Activation functions
2. Bias & Weights
3. Optimization Techniques: RMSprop, Adam, Adagrad
Architectures:
1. Convolutional Neural Networks, CNN
2. Recurrent Neural Networks, RNN & LSTM

Miscellaneous Concepts:

Exploratory Data Analysis
Feature Selection & Feature Extraction
Feature Engineering
Regularization & Hyperparameter Tuning using Random Search, Grid Search Cross Validation

Tools:

Python – Jupyter Notebook & Pycharm
GitHub

Course Content

SNO	TOPIC	Introduction	SUB TOPIC 'S	duration

1	Module : 1	* Introduction to Data Science	* What Is Data Science? The Data Science Workflow Role of a Data Scientist What Is Artificial Intelligence? What Is Supervised Learning?
	Module : 2	* Python Basics for Data Science	* Data Structures in Python Lists, tuples, and dictionaries List comprehensions Manipulating and indexing data structures Practical exercises with data structures
		* Functions and Modules	Defining functions in Python Function arguments and return values Importing and using modules Creating and organising code with functions
		* File Handling	Reading and writing files in Python File modes and operations Handling different file formats (e.g., CSV, JSON) Data extraction from files
		* NumPy for Data Manipulation	Introduction to NumPy Creating NumPy arrays Basic operations with NumPy arrays Indexing and slicing arrays
		* Pandas for Data Analysis	Introduction to Pandas Data frames and series Data manipulation with Pandas Reading and writing data with Pandas
		* Data Visualization with Matplotlib and Seaborn	Introduction to data visualization Creating basic plots with Matplotlib Enhancing plots with Seaborn Customizing and styling visualizations
	Module : 3	* SQL Basics for Data Science	* Introduction to SQL What is SQL? Role of SQL in data science
		* SQL Syntax and Basics	SQL data types Creating tables and databases Inserting, updating, and deleting data SQL statements: SELECT, INSERT, UPDATE, DELETE
		* Querying Data	SELECT statement for data retrieval Filtering data with WHERE Sorting data with ORDER BY Limiting results with LIMIT
		* Filtering and Conditional Logic	Using logical operators (AND, OR, NOT) Combining conditions with parentheses NULL values and handling them
		* Aggregation and Grouping	Aggregate functions: COUNT, SUM, AVG, MIN, MAX GROUP BY clause HAVING clause for conditional aggregation Calculations with aggregate functions
		* Joining Tables	Introduction to table joins (INNER JOIN, LEFT JOIN, RIGHT JOIN) Combining data from multiple tables Self-joins and subqueries Best practices for joining tables
		* Advanced SQL Operations	Subqueries and nested queries Common Table Expressions (CTEs) Window functions and analytic queries Handling duplicates with DISTINCT
		*Real-world Applications	Practical SQL use cases in data science Case studies
	Module : 4	* Data Preliminaries for Data Science	* Data and Measurement Basics: Qualitative vs. Quantitative Data Discrete vs. Continuous Data Categorical vs. Numerical Data
		* Data Types and Scales of Measurement	* Data Cleaning: Handling Missing Data Handling Outliers
		* Data Transformation:	Data Scaling Feature Encoding Feature Engineering
		* Data Splitting:	Split the dataset into training, validation, and test sets. This is crucial for model evaluation and preventing overfitting.
		* Data Normalization:	Ensure that the data follows a normal distribution, which can be important for some statistical analyses. Techniques like log transformations can be used for normalization.
		* Basic Exploratory Data Analysis (EDA):	Descriptive Statistics Data Visualisation Correlation Analysis Data Distribution Outlier Detection Feature Importance
	Module : 5	* Regression –escriptive and Predictive Analytics	* Introduction to Regression Analysis Overview of regression analysis Types of regression: linear, logistic, polynomial, etc. Assumptions and limitations of regression Data preparation for regression
		* Descriptive Regression	Simple linear regression Multiple linear regression Model interpretation and coefficients Assumptions checking and model diagnostics
		* Predictive Regression	Introduction to predictive analytics Train-test split and cross-validation Evaluating predictive models: MSE, R-squared, etc.
		* Regularized Regression	Ridge regression Lasso regression Preventing overfitting in predictive models
		* Real-world Applications	Practical applications of regression in various industries Case studies and guest lectures Ethical considerations in regression analysis
	Module : 6	* Logistic Regression Analysis	* Introduction to Logistic Regression Overview of logistic regression Applications of logistic regression in classification Logistic regression vs. linear regression Data preparation for logistic regression analysis
		* Binary Logistic Regression	Understanding the binary logistic regression model Estimating coefficients: maximum likelihood estimation Model evaluation: ROC curve, AUC, and confusion matrix
		* Multinomial Logistic Regression	Extending to multi-class classification Estimating coefficients in multinomial logistic regression Interpretation of coefficients for multiple classes Model evaluation for multi-class problems
		* Model Evaluation and Metrics	Precision, recall, F1-score Choosing the appropriate evaluation metrics Threshold tuning and ROC analysis Evaluating imbalanced datasets
		* Real-world Applications	Practical applications of logistic regression in various industries Case studies and guest lectures Ethical considerations in logistic regression analysis
		* Predictive Analytics with Logistic Regression	Train-test split and cross-validation Handling imbalanced datasets Ensemble methods with logistic regression Evaluating predictive models for classification
	Module : 7	* Decision Trees	* Introduction to Decision Trees Types of decision trees: classification vs. regression Decision tree terminology: nodes, leaves, branches Data preparation for decision tree analysis
		* Building Decision Trees	Building decision trees from scratch Decision tree splitting criteria: Gini impurity, entropy, and information gain Tree pruning and complexity control Model visualization and interpretation
		* Decision Trees for Classification	Understanding classification trees Handling categorical predictors Predictive modeling using classification trees Model evaluation: confusion matrix, accuracy, precision, recall
		* Decision Trees for Regression	Extending decision trees to regression tasks Predictive modeling using regression trees Model evaluation: Mean Squared Error (MSE), R-squared Handling outliers and non-linear relationships
		* Ensemble Methods with Decision Trees	Introduction to ensemble learning Random Forest: aggregating decision trees Gradient Boosting: boosting decision trees Model comparison and selection
		* Real-world Applications	Practical applications of decision trees in various industries Case studies