We don’t end our course just by providing advanced training we will provide all our students who enrolled for our advanced Data science training Institute in BTM Layout Real-time Projects exploration Opportunity. So one can easily face & solve any kind of challenges while working in Real-time Projects in any Organization. Prwatech also offering a dedicated YouTube channel in case any of our students missed any classes that can easily access our YouTube channel to cover the missing topics.
Benefits of Data Science Training in BTM Layout @Prwatech
- 100% Job Placement Assistance
- 24*7 Supports
- Support after completion of Course
- Mock tests
- Free Webinar Access
- Online Training for Data Science
- Interview Preparation
- Real Times Projects
- Course Completion Certificate
- Weekly Updates on Latest news about the technology via mailing System
- During various sessions, one can learn the use of different tools of this framework.
What Else We Do Provide
- Real-Time Project
- Video Recording
- Assignment
- Technical Notes
- Mock Test
- Online Test
- POC
- 100% Job Assistance
Modules of Data Science Training in BTM
Module 1: Python Essentials
Learning Objective: To understand why Python is a useful scripting language for developers. To learn how to design and program Python applications, join online python training course from prwatech.
Introduction
- What is Python..?
- A Brief history of Python
- Why Should I learn Python..?
- Installing Python
- How to execute Python program
- Write your first program
Variables & Data Types
- Variables
- Numbers
- String
- Lists, Tuples & Dictionary
Conditional Statements & Loops
- If…statement
- If…else statement
- elif…statement
- The while…Loop
- The for….Loop
Control Statements
- Continue statement
- Break statement
- Pass statement
Functions
- Define function
- Calling a function
- Function arguments
- Built-in functions
Modules & Packages
- Modules
- How to import a module…?
- Packages
- How to create packages
- Classes & Objects
Introduction about classes & objects
- Creating a class & object
- Inheritance
- Methods Overriding
- Data hiding
Files & Exception Handling
- Writing data to a file
- Reading data from a file
- Read and Write data from CSV file
- Try…except
- Try…except…else
- Finally
- OS module
Module 2: Statistics
Learning Objective: Demonstrate knowledge of statistical data analysis techniques utilized in decision making. Apply principles of Data Science to the analysis of problems. Be able to restate an investigative question in terms of a statistical model or algorithm.
Inferential statistics
- Standard error
- Confidence interval
- Hypothesis testing
- p-value
Sample vs. population mean
Probability and statistics
- Probability density functions
- Poisson Process
- Law of large numbers
- Normal distribution
- Sampling distribution
- Mean
- Variance of Bernoulli
- Regression
Module 3: Introduction to Machine Learning (ML)
Learning Objective: The purpose of machine learning is to discover patterns in your data and then make predictions based on those often, complex patterns to answer business questions, and help solve problems. Machine learning helps analyze your data and identify trends.
Introduction
- What is Machine learning?
- Overview about sci-kit learn and tensor flow
- Types of ML
- Some complementing fields of ML
- ML algorithms
- Machine learning examples
NumPy Arrays
- Creating multidimensional array
- NumPy-Data types
- Array attributes
- Indexing and Slicing
- Creating array views and copies
- Manipulating array shapes
- I/O with NumPy
Working with Pandas
- Installing pandas
- Pandas data frames
- Pandas Series
- Data aggregation with Pandas Data Frames
- Concatenating and appending Data Frames
- Joining Data Frames
- Handling missing data
Python Regular Expressions
- What are regular expressions?
- The match Function
- The search Function
- Matching vs searching
- Search and Replace
- Extended Regular Expressions
- Wildcard
Python Oracle Database Access
- Install the cx_Oracle and other Packages
- Create Database Connection
- CREATE, INSERT, READ, UPDATE and DELETE Operation
- DML and DDL Operation with Databases
- Performing Transactions
- Handling Database Errors
- Disconnecting Database
Regression based learning
- Simple regression
- Multiple regression
- Logistic regression
- Predicting house prices with regression
Clustering based learning
- Definition
- Types of clustering
- The k-means clustering algorithm
Data mining
- Introducing data mining
- Decision Tree
- Affiity Analysis
- Clustering
Introducing matplotlib
- Bar Charts
- Line Charts
- Scatter plots
- Bubble charts
Working with openCV
- Setting up opencv
- Loading and displaying images
- Applying image filters
- Tracking faces
- Face recognition
Module 4: Performing predictions with Linear Regression
Learning Objective: The basic objective of this course is to learn How to calculate a simple linear regression step-by-step, How to perform all of the calculations using a spreadsheet, How to make predictions on new data using your model, A shortcut that greatly simplifies the calculation.
Simple linear regression
- Multiple regression
- Training and testing model
Introduction to Python
- Installation of Python framework and packages: Anaconda & pip Writing/
- Running python programs using Spyder Command Prompt Working with Jupyter
Notebooks.
- Creating Python variables
- Numeric, string and logical operations
- Data containers: Lists, Dictionaries, Tuples & sets Practice assignment
Iterative Operations & Functions in Python
- Writing for loops in Python
- While loops and conditional blocks List/Dictionary comprehensions with loops
- Writing your own functions in Python Writing your own classes and functions
Data Handling in Python using NumPy & Pandas
- Introduction to NumPy arrays, functions & properties Introduction to Pandas &
Data frames
- Importing and exporting external data in Python Feature engineering using
Python
Data Science & Machine Learning in Python
- Converting business problems to data problems
- Understanding supervised and unsupervised learning with examples
- Understanding biases associated with any machine learning algorithm Ways of
- Reducing bias and increasing generalization capabilities Drivers of machine
- Learning algorithms
- Cost functions
- Brief introduction to gradient descent
- Importance of model validation
- Methods of model validation
- Cross validation & average error
Generalized Linear Models in Python
- Linear Regression
- Regularization of Generalized Linear Models
- Ridge and Lasso Regression
- Logistic Regression
- Methods of threshold determination and performance measures for Classification score models
Tree Models using Python
- Introduction to decision trees
- Tuning tree size with cross validation Introduction to bagging algorithm Random
Forests
- Grid search and randomized grid search ExtraTrees (Extremely Randomised
Trees) Partial dependence plots
- Support Vector Machines (SVM) & kNN in Python
- Introduction to idea of observation based learning Distances and similarities
k Nearest Neighbors (kNN) for classification Brief mathematical background on
SVM Regression with kNN & SVM
Unsupervised learning in Python
- Need for dimensionality reduction
- Principal Component Analysis (PCA) Difference between PCAs and Latent
- Factors Factor Analysis
- Hierarchical, K-means & DBSCAN Clustering
Text Mining in Python
- Gathering text data using web scraping with urllib
- Processing raw web data with BeautifulSoup
- Interacting with Google search using urllib with custom user agent collecting twitter data with
- Twitter API
- Naive Bayes Algorithm
- Feature Engineering with text data
- Sentiment analysis
Best Data Science Training institute in BTM Layout Reviews
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