Placement Oriented Diploma Program​
Eligibility: BE/ M.Tech/MCA
Duration: 240 Hours
Mode of Training: Offline & Online
Intermediate
Cranes Varsity’s 240-hour intensive program is your gateway to a rewarding career in Artificial Intelligence and Machine Learning. Whether you’re a data enthusiast or a seasoned professional, our comprehensive curriculum, delivered through flexible online and offline modes, equips you with the skills and knowledge to excel in this rapidly evolving field.
Core Modules:
- RDBMS using MySQL: Gain a solid foundation in relational database management systems, essential for handling large datasets.
- Python for Data Science: Master Python programming, the go-to language for data science and machine learning.
Analytics Specialization:
- Exploratory Data Analysis using Pandas: Learn to explore and analyze data using the powerful Pandas library.
- Data Analysis and Visualization using Excel and Power BI: Gain proficiency in data analysis and visualization techniques using popular tools like Excel and Power BI.
AI Specialization:
- Machine Learning for Data Science: Dive deep into the world of machine learning, covering key algorithms and techniques for building intelligent systems.
With over 25 years of industry experience, Cranes Varsity offers a unique blend of theoretical knowledge and practical application. Our expert instructors and state-of-the-art training facilities provide an immersive learning experience
Benefits of this program
- Internship with AICTE Registered company
- Concept to Project experience
- Â Exposure to real time scenarios and challenges
- Certificate of Participation
Generic Modules
- Introduction to databases and RDBMS,
- Database creation, concept of relation and working examples
- Creating tables. Design view of the table, Alter table operations & Key Constraints
- Read, update and delete operations on tables. Working with nulls
- Querying tables: Select statement, examples and its variations
- Filtering, Sorting, Predicates and working examples
- Joins in SQL and working examples
- Insert, Update, Delete operations and working examples
- Scalar functions in SQL and working examples
- SQL set based operations and data aggregation Sub-queries in SQL
- Normalization and de- normalization: Views and Temporary tables Transactions in SQL
- SQL programming Creating stored procedures, Cursors in SQL
- Introduction to Python Python
- Data types and Conditions
- Control Statements
- Python Functions
- Default arguments
- Functions with variable number of args
- Scope of Variables
- Global specifier
- Working with multiple files
- List and Tuple
- List Methods
- List Comprehension
- Map and filter functions
- String
- List comprehension with conditionals
- Set and Dictionary
- Exception Handling
- File Handling
- Object Oriented Programming
- Overloading Operator
- Inheritance
- Testing Fundamentals
- Unit testing
- Collection Frameworks
Analytics Specialization
- Introduction to excel
- Viewing, Entering, and Editing Data
- Introduction to Data Quality
- Data Preparation
- Converting Data with Value and Text
- Apply logical operations to data using IF
- Analyzing Data Using Spreadsheets
- Pivot Table and Power Pivot
- What-if Analysis and Forcasting.
- Data Visualization in Excel
- Interactive dashboard creation
- Power Query
- Introduction to Power BI
- Power BI Desktop
- Data Modelling in Power BI
- Data transformation
- DAX Functions
- Creating visualization
- Data Visualization Best practices
- Creating Reports
- Creating Dashboards
- NumPy
- Vectorization
- Broadcasting
- Slicing of Matrices
- Filtering
- Array Creation Functions
- NumPy Functions across axis
- Stacking of arrays
- Matrix Calculation
- Pandas Series
- Data Cleaning
- Handling Missing
- Data Pandas Data frame
- Selection Data (loc, iloc)
- Filtering Data Frames
- Working with Categorical Data
- Grouping & Aggregation
- Merging Data Frame (concat, merge)
- Sorting Data Frames
- Importing csv files
- Importing Excel Files
- Creating graphs using Matplotlib
- Customizing Plots
- Saving Plots
- Scatter Plot, Line Graph
- Bar Graph, Histogram
- Subplots, Seaborn
AI Specialization
- Understand what is Machine Learning
- Regression
- Logistic regression
- Supervised machine learning
- Simple linear regression
- Naïve Bayes Classification
- Unsupervised machine learning
- Multiple linear regression
- Decision tress and its types
- Train test split the data
- Performance measure for regression
- K Nearest Neighbour Classification
- ML Workflow for project implementation
- Classification and types
- Performance Measure for Classification
- Random Forest
- Clustering and types
- Evaluate clustering results, Elbow Plot
Project stream:
- Apply statistical methods to make decision in various business problems, including bank, stock market etc.
- Â Apply regression to predict future flight price
- Â Apply classification to classify customer
- Use clustering to cluster banking customers
Platform:
- Anaconda Distribution Jupyter, Spyder, MySQL, Google Colab, Pycham
- Â MS Excel, POWER BI
Data science became the most in-demand skill-set of the 21st century due to the increased amount of data generated by the online users and collecting same by most the companies, as data collected by these companies has to be utilized effectively to scale up the business, the need fora skilled data scientist is very high. The internship program in data science by cranes varsity provides the interns with a varied skill-set for one to master him/her self in the domain of data science.
During the internship program, the interns will get good exposure to Python programming concepts, Machine learning techniques and will also learn about the Project life cycle of data science. These skill-sets are learned to enable our interns to stand out during the interview process and can expect better job opportunities. Data Science is a very popular field and there are a ton of companies looking for people with this skill set. To give you just one example, we have over 700 open positions right now on our own platform, and that’s just one company!Â
Course Objectives
- Understanding language components, the IDLE environment, control flow constructs, strings, I/O, collections, classes, regular expressions and OOP
- The course is supplemented with many hands on
- Understanding the design & development of models using Machine learning
- Understanding the design & development of models using Pandas , Matplotlib & Numpy
Tools and Resources
Python 3.8
Platform: Linux / Windows 7 and above
Course Content (Syllabus)
Day 2:
Control Flow
- Relational Operators
- if…else statement
- if…elif…else statement
- Logical operators
- While Loops
- break and continue statement
- Loops with else statement
- pass statement
- Python for loop
- Range Function
Day 3:
Lists
- Creating List
- Accessing elements from List
- Inserting and Deleting Elements from List
- List Slicing
- Joining two lists
- Repeating sequence
- Nested List
- Built-in List Methods and Functions
- Searching elements in List
- Sorting elements of List
- Implementing Stack using List
- Implementing Queue using List
- Shallow and Deep copy
- List Comprehensions
- Conditionals on Comprehensions
Day 4:
Functions
- Defining Functions in Python
- Function Argument
- Single Parameter Functions
- Function Returning single Values
- Functions with multiple parameter
- Function that return Multiple Values
- Functions with Default arguments
- Named arguments
- Scope and Lifetime of Variables
- global specifier
- Functional programming   tools:    map(), reduce() and filter()
- Lambda: short Anonymous functions
- Creating and importing modules
- Programming Examples & Assignments
- Recursion
Day 5:
Python Data Structures
- Python Set
- Creating Set
- Adding/Removing elements to/from set
- Python Set Operations : Union, Intersection, Difference and Symmetric Difference
- Python Tuple
- Creating Tuple
- Understanding Difference between Tuple and List
- Accessing Elements in Tuple
- Python Dictionary
- Creating Dictionary
- Accessing / Changing / Deleting Elements in Dictionary
- Built-in Dictionary Methods and Functions
Day 6:
Exception Handling
- Understand Exception
- Handling exception
- try and except blocks
- multiple except blocks for a single try block
- finally block
- Raising exceptions using raise
Day 7:
File Handling
- Introduction to File handling
- File opening modes
- Reading data from file
- Writing data to file
Day 8:
Object Oriented Programming
- Creating Class
- Creating Objects
- Method Invocation
- Understanding special methods
-    init    method
-    del    method
-    str    method
- Operator Overloading
- Overloading arithmetic operators
- Overloading relational operators
- Inheritance
Day 9:
Module 1 – Data Analysis and Visulization
- NumPy
- Vectorized
- Operation
- Subsetting
- Matrix Calculation
Day10:
Pandas
- Pandas Series
- Pandas Dataframe
- Importing Data
Day11:
Data cleaning with pandas
- Data Cleaning
- Handling Missing Data
Day12:
Matplotlib
- Creating graphs using Matplotlib
- Customizing Plots
- Saving Plots
Day13:
Module 2-Machine Learning
- Understand what is Machine Learning
- Supervised Learning
- Unsupervised Learning
Day14:
Introduction to Regression
- Regression
- Linear Regression with Single Variable
- Multiple Linear Regression
Day15:
Training Data Set
- Training and Testing Data
- Handling Categorical Data
- K-Fold Cross Validation
Day16:
Logistic Regression
- Classification
- Logistic Regression – Binary classification
- Logistic Regression – Multiclass classification
Day17:
Decision Tree
- Decision Tree Classifier
- Support Vector Machine
- KNN Classifier
Mini -Projects:
- Python project development based onmatplotlib&pandas.
- Python project development based onNumpy.
Hiring Partners
FAQs
Is cranes Varsity training available online?
Yes, Cranes Varsity training is available through online
How does online training work?
Our Online training is Instructor-Led live online sessions
Do you provide training course material?
Yes, we will provide training course material for each module
Do you offer weekend classes?
Yes, we offer weekend classes as well evening classes.
