Best Data Science Using Python Institute in Delhi NCR
CETPA Infotech provides comprehensive Data Science training using Python. Training in Noida with 100% placement assistance. Python is a highly interactive and open-source object-oriented language that is efficiently integrated with machine learning and data science. The process of examining the data, statistical computing, and data analysis can be done using Python. Importing data, data reporting using visualization, data manipulation and data modeling can be done efficiently using Python.
The Data Science Python Program will enhance your skills with in-depth knowledge of the different libraries and packages to perform data analysis, web scraping, data visualization, machine learning, and natural language processing using Python. It covers data science from scratch. There is a huge demand for data scientists across all industries, which makes this course suited for participants at all levels of experience. This course is suitable for:
- Graduates who want to build a career in data science and analytics.
- IT professionals who are interested making a career in data science.
- Analytics professionals who want to enhance data science skills using python.
- Anyone who has interest in this field.
There are numerous reasons which make CETPA one of the best data science (using Python) training institutes in Noida. Some of the reasons include:
- Training partners of companies like Microsoft, Oracle, Panasonic, Nuvoton, Autodesk, and many more.
- Best Data Science faculty with real time experience in Data Science.
- Goal-oriented programs.
- Assured placement assistance.
- High- Quality Study Material.
- World-class infrastructure with latest facilities.
- Opportunity to work on real time projects.
- Internationally accepted and recognized certifications.
- Apart from Classroom Training, online training is also provided.
- Flexible Batch Size and Timings that is you can choose the timings of your batch according to your convenience.
- Soft-skill development like communication skills, interview preparation and so on.
- Doubt clearing session at the end of the training.
CETPA delivers best-in-class data science using Python training with the help of industry experts. We are aware of industry needs and, as a result, provide more practical training programs. Our team offers classroom training, online training, as well as corporate training services. We designed our syllabus to correspond to real-world requirements at both the beginner and advanced levels. Our training is delivered either during the weekdays or weekends, depending on the participant’s requirements.
Our curriculum includes:
- Installation of Python and Eclipse IDE
- Functional Programming
- Object Oriented Programming
- Modules and Packages
- Exception Handling
- File Handling
- Work with MongoDB.
- Introduction of Data Science
- Introduction of Essential Python Libraries
- Installation of Numpy, Matplotlib, Pandas, Scikit-learn, Ipython, and Jupyter.
- Data Loading
- Data cleaning and preparation
- Time series
- Data Aggregation and Group Operations Data Analysis Examples
CETPA just not only train you in technical skills, but also share real-time execution expertise to gain knowledge and practical skills to enlighten one’s career.
Mode/Schedule of Training:
CETPA, The Best Online Data Science Using Python Training Institute in Delhi NCR offers courses in following modes.
|Delivery Mode||Location||Course Duration||Schedule (New Batch Starting)|
|Classroom Training (Regular/ Weekend Batch)||*Noida/ Lucknow *Dehradun /Roorkee||4/6/12/24 weeks||New Batch Wednesday/ Saturday|
|*Instructor -Led Online Training||Online||40/60 Hours||Every Saturday or as per the need|
|*Virtual Online Training||Online||40/60 Hours||24×7 Anytime|
|College Campus Training||India or Abroad||40/60 Hours||As per Client’s need|
|Corporate Training (Fly a Trainer)||Training in India or Abroad||As per need||Customized Course Schedule|
CURRICULUM OF CORE & ADVANCED PYTHON
- History & need of Python
- Application of Python
- Advantages of Python
- Disadvantages of Python
- Installing Python
- Program structure
- Interactive Shell
- Executable or script files.
- User Interface or IDE
- Working with Interactive mode
- Working with Script mode
- Python Character Set
- Python Tokens, Keywords, Identifiers, Literals, Operators
- Variables and Assignments
- Input and Output in Python
- Data Types
- Mutable and Immutable
- Introduction to Python String
- Accessing Individual Elements
- String Operators
- String Slices
- String Functions and Methods
- Introduction to Python List
- Creating List
- Accessing List
- Joining List
- Replicating List
- List Slicing
- Introduction to Tuple
- Creating Tuples
- Accessing Tuples
- Joining Tuples
- Replicating Tuples
- Tuple Slicing
- Introduction to Dictionary
- Accessing values in dictionaries
- Working with dictionaries
SET AND FROZENSET
Introduction to Set and Frozenset
- Creating Set and Frozenset
- Accessing and Joining
- Replicating and Slicing
- Arithmetic Operators
- Relational Operators
- Logical Operators
- Membership Operators
- Identity Operators
- Bitwise Operators
- Assignment Operators
- Operators Precedence
- Evaluating Expression
- Type Casting
PROGRAM CONTROL FLOW
- The if Statement
- The if-else Statement
- The if-elif Statement
- Nested if Statements
- Python Indentation
- Looping and Iteration
- The For Loop
- The While Loop
- Loop else Statement
- Nested Loops
- Break and Continue
- The Range Function
- Introduction to range()
- Types of range() function Use of range() function
INTRODUCTION TO FUNCTIONS
- Introduction to Functions
- Using a Functions
- Python Function Types
- Structure of Python Functions
- E.g.map, zip, reduce, filter, any,chr, ord, sorted, globals, locals, all, etc.
User Defined Functions
- Structure of a Python Program w.r.t. UDF
- Types of Functions
- Invoking UDF
- Flow of Execution
- Arguments and Parameters
- Default Arguments, Named Arguments
- Scope of Variables
- Lambda function
- Use of recursion function
MODULES AND PACKAGES
- Importing Modules in Python Programs
- Working with Random Modules
- E.g.-builtins, os, time, datetime, calendar, twilio, smtp, pillow.
User Defined Modules
- Structure of Python Modules
Text and Bytes files
- Opening a file
- Reading and Writing Files
- Other File tools
FORMAT CLASSES AND OBJECTS
- Classes as User Defined Data Type
- Objects as Instances of Classes
- Creating Class and Objects
- Creating Objects By Passing
- Values Variables & Methods
- Default Exception and Errors
- Catching Exceptions
- Raise an exception
- Try…except statement
- Raise, Assert, Finally blocks
- User defined exception
INTRODUCTION TO OOPS
- Procedural Vs Modular Programming
- The Object Oriented Programming
- Data Abstraction
- Data Hiding
- SIntroduction to MySQL
- PYMYSQL Connections
- Executing queries
- Transaction Handling error
- Tkinter programming
- Tkinter widgets
- All Widget
- Revisiting Python
- List and dictionary comprehension
- Programming assignment
INTRODUCTION TO DATA ANALYTICS
- Why Analytics?
- Traditional Data Management
- Types of Analytics
- Dimensions and measures
- Why learn Python for data analysis?
LIBRARIES FOR DATA ANALYTICS
- Numpy, Scipy, Pandas
- Matplotlib, Seaborn
- Mean, Median, Mode
- Bias -variance dichotomy
- Sampling and t-tests
- Sample vs Population statistics
- Random Variables
- Probability distribution function
- Expected value
- Binomial Distributions
- Normal Distributions
- Central limit Theorem
- Hypothesis testing
- Z-Stats vs T-stats
- Type 1 type 2 error
- Chi Square test
- ANOVA test and F-stats
- Create Documentation
- Code mode
- Markdown mode
- Heading mode
- Creating NumPy arrays
- Indexing and slicing in NumPy
- Downloading and parsing data
- Creating multidimensional arrays
- NumPy Data types
- Array tributes
- Indexing and Slicing
- Creating array views copies
- Manipulating array shapes I/O
- Introduction to SciPy
- Create function
- modules of SciPy
- Using multilevel series
- Series and Data Frames
- Grouping, aggregating
- Merge DataFrames
- Generate summary tables
- Group data into logical pieces.
- Manipulate dates
- Creating metrics for analysis
- Data wrangling
- Merging and joining
- Analytics Vidhya dataset- Loan Prediction Problem
- Data Mugging using Pandas
- Building a Predictive Model
- Scatter plot
- Bar charts, histogram
- Legend title Style
- Figures and subplots
- Plotting function in pandas
- Labelling and arranging figures
- Save plots
- Style functions, Color palettes
- Distribution and Categorical plots
- Regression plots
- Axis grid objects
- Scraping Webpages
- Beautifulsoup package
- Real time project
INTRODUCTION TO ML
- What is ML? And Why ML?
- Introduction to Supervised ML
- Introduction to Unsupervised ML
- Mathematical Background for ML
- Matrix ops Probability Theory (Bayes’ Theorem)
- ML Glossary- Variable types, k-fold
- Data split & hyper parameter
- Real time projects
- Python programming
- Introduction to ML
- What is ML? Why ML?
- Introduction to Supervised ML
- Introduction to Unsupervised ML
- Difference Between Al|DLIML
Tools required for development
Anaconda, Jupyter NB/Google Colab/Spyder
- ML libraries
- Numpy: Introduction to Numpy
- pandas:Introduction] DataFrame Loading
- datasets | Loading data from database | pandas Operation.
- Matplotlib: Introduction| Line Chart |Pie Chart
- Scatter Plot | Bar chart |Histogram
- Sklearn:: Introduction |Sklearn-API]
- ML Glossary
- Variable types, k-fold CV, AUC,
- F1 score,Overfitting/Underfitting,
- Generalization, ROC |Confusion matrix
- Mathematical Background for ML- Matrix ops
- Probability Theory (Bayes’ Theorem)
- Statistical knowledge for ML- Mean, Median,
- Mode, Z-scores, bias-variance dichotomy.
- Exploratory Data analysis using Visualisation
- Scikit-learn Library for ML
- Code Exercises
Steps of Machine Learning
- Data Collection. The quantity & quality of your data
- dictate how accurate our model is…..
- Data Preparation. Wrangle data and prepare it for
- training Data wrangling using Pandas|Preprocessing
data and feature engineering Data split
- Choose a Model.
- Train the Model….
- Evaluate the Model…..
- 6-Parameter Tuning] hyper parameter training
- Make Predictions.
- Introduction Maths behind Supervised Machine
- Learning and Algo.
- Linear Regression
- Multi-Linear Regression
- Decision Tree Regressor
- Support Vector Regressor
- Logistic Regression
- KNN-K Nearest Neighbors
- Support Vector Classifier(SVM-SVC)
- Decision Tree Classifier(DTC)
- Random Forest
- Naïve Bayes
- Ensemble Learning
Unsupervised Machine Learning
- Mathematics behind Clustering
- Implementation of K-mean Clustering
- Implementation of H-clustering
- Code Exercises
- Apiori rule
- Principle Component Analysis(PCA).
- IBM ATTRITION RATE PREDICTION USING MACHINE LEARNING
- COVD-19 PATIENT OUTCOME PREDICTION USING ML
- ESTIMATE THE ONLINE SALES OF A E-COMMERCE RETAIL FIRM USING ML
- GUI BASED MACHINE LEARNING APPLICATION TO CLASSIFY THE PLANT SPECIES OF IRIS FLOWER
- PREDICT THE CHURN RATE IN A TELECOM COMPANY USING ML
- MALL CUSTOMER SEGMENTATION USING ML
- MARKET BASKET ANALYSIS AND ASSIT A SHOPPING MALL TO STACK PRODUCT
- PREDICT AND ESTIMATE CAR RE-SALE VALUE USING MACHINE LEARNING
- WORKING ON INBULIT DATASETS
- PREDICTION CLASSIFICATION OF HANDWRITTEN DIGITS