PG Diploma in Data Science and Machine Learning
100% JOB Assured with Globally Accepted Certificate
Intermediate
PG Diploma in Data Science and Machine Learning
100% JOB Assured with Globally Accepted Certificate
Overview
Data Science Online Course
Description
The PG Diploma in Data Science Online Course and Machine Learning is a five-month professional program that provides in-depth data science knowledge and expertise.
Cranes mentors engineers in all critical disciplines to assist them in excelling at designing Data Science based applications that meet industry standards.
Cranes provide students with a structured framework to help them develop technical skills and knowledge. The lectures are well-planned and delivered with examples to make them more interesting and understandable. We want to help students develop a much broader range of mental representations of knowledge.
Cranes Varsity is considered as the best Data Science Course (Available Online) which offers services from training to placement as part of the Data Science training program with over 400+ participants placed in various multinational companies including Genpact, Ernst & Young, Capgemini, Vodafone, CGI, Wipro, Tata Elxsi, IBM, Lumen Technologies, Tech Mahindra, Birla Soft, HTC, Happiest Minds, Western Digital, Mearsk Global, Koireader, K7 Computing, Mphasis, Atos, Latent View, etc.
Data Science Online Course with Placement
We offer the highest quality teaching, assessment and placement support through our Data Science course. The course is designed to make a novice into an expert from developing Python programming, writing queries on SQL to building Machine learning & Deep Learning Models and Cloud computing. Our Lead mentors are industry experts and have been associated with us for decades.
If you’re looking toward building your career in Data Science and are interested in getting the Data Science Training with Certification & Placement, then Cranes Varsity is the right destination for realizing your aspirations and growing on your Career ladder.
Data Science using Python training course syllabus is classified into modules that help students better understand the subject. Which are listed below:
Master Data Science with Our Online/Offline Course
Cranes Varsity offers a comprehensive online and offline Data Science course designed to equip you with the skills and knowledge needed to excel in this rapidly evolving field.
PG Diploma in Data Science and Machine Learning Course is comprehensive placement assured program which covers RDBMS, Python, Exploratory Data Analysis using Pandas, Machine Learning, Deep Learning, Excel and Power BI.
After completing a PG Diploma in Data Science and Machine Learning Certification Course at Cranes Varsity, Bangalore, you will possess a diverse set of skills and capabilities. You will be capable of doing Data Analysis and Visualization, Machine Learning Modeling, Deep Learning, Predictive Analytics, Data Preprocessing & Data Storytelling. This course is offered in both online and offline mode.
Developing expertise in several key areas. Firstly, learning Data Manipulation and Analysis techniques using programming language like Python. You will gain proficiency in Statistical Analysis and Data Visualization to extract insights from complex datasets. Machine learning skills will enable you to build predictive models, classification algorithms, and clustering techniques. Deep learning will empower you to work with neural networks for advanced tasks like image and text analysis. Additionally, you will acquire knowledge in data preprocessing, model evaluation, and deployment. These skills will equip you to tackle real-world challenges and unlock exciting opportunities in data-driven industries.
These capabilities will enable you to pursue various roles in the field of Data Science and Machine Learning, such as Data Scientist, Machine Learning Engineer, Data Analyst, or AI Specialist, across industries ranging from Finance and Healthcare to E-commerce and Marketing.
Cranes Varsity offers a comprehensive online and offline Data Science course designed to equip you with the skills and knowledge needed to excel in this rapidly evolving field.
PG Diploma in Data Science and Machine Learning Course is comprehensive placement assured program which covers RDBMS, Python, DAV using Numpy & Pandas, Machine Learning, Deep Learning and Tableau.
After completing a PG Diploma in Data Science and Machine Learning Certification Course at Cranes Varsity, Bangalore, you will possess a diverse set of skills and capabilities. You will be capable of doing Data Analysis and Visualization, Machine Learning Modeling, Deep Learning, Predictive Analytics, Data Preprocessing, Data Storytelling, This course is offered in both online and offline mode
Developing expertise in several key areas. Firstly, learning data manipulation and analysis techniques using programming languages like Python and R. You will gain proficiency in statistical analysis and data visualization to extract insights from complex datasets. Machine learning skills will enable you to build predictive models, classification algorithms, and clustering techniques. Deep learning will empower you to work with neural networks for advanced tasks like image and text analysis. Additionally, you will acquire knowledge in data preprocessing, model evaluation, and deployment. These skills will equip you to tackle real-world challenges and unlock exciting opportunities in data-driven industries.
These capabilities will enable you to pursue various roles in the field of data science and machine learning, such as data scientist, machine learning engineer, data analyst, or AI specialist, across industries ranging from finance and healthcare to e-commerce and marketing.
PG Diploma in data science and machine learning is comprehensive program which covers RDBMS, Python, DAV using Numpy & Pandas, Machine Learning, Deep Learning and Tableau.
After completing a PG Diploma in Data Science and Machine Learning, you will possess a diverse set of skills and capabilities. You will be capable of doing Data Analysis and Visualization, Machine Learning Modeling, Deep Learning, Predictive Analytics, Data Preprocessing,Data Storytelling,
Developing expertise in several key areas. Firstly, learning data manipulation and analysis techniques using programming languages like Python and R. You will gain proficiency in statistical analysis and data visualization to extract insights from complex datasets. Machine learning skills will enable you to build predictive models, classification algorithms, and clustering techniques. Deep learning will empower you to work with neural networks for advanced tasks like image and text analysis. Additionally, you will acquire knowledge in data preprocessing, model evaluation, and deployment. These skills will equip you to tackle real-world challenges and unlock exciting opportunities in data-driven industries.
These capabilities will enable you to pursue various roles in the field of data science and machine learning, such as data scientist, machine learning engineer, data analyst, or AI specialist, across industries ranging from finance and healthcare to e-commerce and marketing.
Data Science Online Course Modules
- RDBMS using MySQL
- Python for Data Science
- Advanced Python (Testing and Web Scraping)
- Exploratory Data Analysis using Pandas
- Mathematics and Statistics for Data Science
- Machine Learning using sklearn
- Machine Learning model Improvement
- Deep Learning using Tensor Flow
- Data Analysis and Visualization using Tableau
- Natural Language Processing
- Apply statistical methods to make decisions in various business problems, including bank, stock markets, etc.
- Apply regression to predict future flight price
- Apply classification to classify customer
- Use clustering to cluster banking customers
- Computer vision projects like Face recognition, Image Quality Improvement, etc.
- Anaconda Distribution Jupyter, Spyder
- Tableau
- Google Colab
Course Content
Generic:
- 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
- EBS(Elastic Block Storage),VPC
- EBS volumes and Snapshots
- RDS
- 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
- NumPy
- Vectorization
- Broadcasting
- Slicing of Matrices
- Filtering
- Array Creation Functions
- NumPy Functions across axis
- Stacking of arrays
- Matrix Calculation
- 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
- Matplotlib
- Introduction to excel
- Viewing, Entering, and Editing Data
- Introduction to Data Quality
- Intro to Analyzing Data Using Spreadsheets
- Converting Data with Value and Text
- Apply logical operations to data using IF
- Charting techniques in Excel
- Interactive dashboard creation
- Data analytics project using Excel
- Importing Data in Power BI
- Data Preparation in Power BI
- Data Modelling in Power BI
- Filtering Visualizing Data Reports in Power BI
- Introduction to DAX in Power BI
- Logarithm
- Python Scipy Library
- Data Preprocessing
- Standard Deviation
- Probability and Distribution
- Handling missing data
- Descriptive and Inferential Statistics
- Binomial Theorem
- Onehot Encoding
- Mean, Median, Mode
- Hypothesis testing
- Label encoding
- Percentile,
- Inferential Statistics
- Standardization and normalization
- Log Normal Distribution
- Chi-square test, T test
- Binning
- Mean Absolute Deviation,
- Ordinal, frequency encoding
- Transformation
- Handson Examples
- Case study: To perform Data cleaning and statistical analysis
- Optimizing regression models with forward elimination, grid search cv
- Improving classification models with Ensemble modeling
- Boosting techniques,: ADA boost, Gradient Boost, XG boost
- Regularization L1 and L2 regularization
- Random Forest, Bagging
- Dimensionality Reduction with PCA
- Clustering and types
- Evaluate clustering results, Elbow Plot
- Train test split the data
- Kmeans Clustering
- Hierarchical clusterin
- Hyperparameter Tuning
- ML Project
- ML Project
- Stacking and Voting
- What is Deep Learning
- Hidden Layers
- Building project based on CNN
- Deep Learning Methods
- Activation Function
- Tensorflow, pytorch, Keras
- Deep Learning Application
- Forward and Backward propagation
- Batch Normalization, dropout
- Artificial Neural Network
- Deep Learning Libraries
- Performance measure for ANN
- CNN architecture
- CNN for computer vision
- Need for Hardwares in Deep learning
- Computer vision basics
- OpenCV
- Working with Images
- Edge detection
- Filtering
- Object detection
- Transfer Learning
- Pretrained models,
- Restnet50, Imagenet, Mobilenet
- Object Oriented Programming
- Overloading Operator
- Inheritance
- Regular Expression
- Finding Patterns of Text
- Meta characters
- Testing Fundamentals
- Unit Testing with Pytest
- Working with JSON
- Decorators
- UI Development with Tkinter
- Containers
- Iterators
- UI development Mini Project
- 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
- EBS(Elastic Block Storage),VPC
- EBS volumes and Snapshots
- RDS
- 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
- 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
- NumPy
- Vectorization
- Broadcasting
- Slicing of Matrices
- Filtering
- Array Creation Functions
- NumPy Functions across axis
- Stacking of arrays
- Matrix Calculation
- 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
- Matplotlib
- Logarithm
- Python Scipy Library
- Mean Absolute Deviation,
- Standard Deviation
- Probability and Distribution
- Normal Distribution and Z Score
- Descriptive and Inferential Statistics
- Binomial Theorem
- Visualizing Data
- Mean, Median, Mode
- Hypothesis testing
- Variance: ANOVA
- Percentile,
- Inferential Statistics
- Statistical Significance
- Log Normal Distribution
- Chi-square test, T test
- Data Preprocessing
- Standardization and normalization
- Ordinal, frequency encoding
- Transformation
- Tableau Introduction
- Traditional Visualization vs Tableau
- Tableau Architecture
- Working with sets
- Creating Groups
- Data types in Tableau
- Connect Tableau with Different Data Sources
- Visual Analytics
- Parameter Filters
- Cards in Tableau
- Charts, Dash-board
- Joins and Data Blending
- Tableau Calculations using Functions
- Building Predictive Models
- Dynamic Dashboards and Stories
- Logarithm
- Python Scipy Library
- Data Preprocessing
- Standard Deviation
- Probability and Distribution
- Handling missing data
- Descriptive and Inferential Statistics
- Binomial Theorem
- Onehot Encoding
- Mean, Median, Mode
- Hypothesis testing
- Label encoding
- Percentile,
- Inferential Statistics
- Standardization and normalization
- Log Normal Distribution
- Chi-square test, T test
- Binning
- Mean Absolute Deviation,
- Ordinal, frequency encoding
- Transformation
- Handson Examples
- Case study: To perform Data cleaning and statistical analysis
- Introduction to excel
- Viewing, Entering, and Editing Data
- Introduction to Data Quality
- Intro to Analyzing Data Using Spreadsheets
- Converting Data with Value and Text
- Apply logical operations to data using IF
- Charting techniques in Excel
- Interactive dashboard creation
- Data analytics project using Excel
- Object Oriented Programming
- Overloading Operator
- Inheritance
- Regular Expression
- Finding Patterns of Text
- Meta characters
- Testing Fundamentals
- Unit Testing with Pytest
- Working with JSON
- Decorators
- UI Development with Tkinter
- Containers
- Iterators
- UI development Mini Project
- Optimizing regression models with forward elimination, grid search cv
- Improving classification models with Ensemble modeling
- Boosting techniques,: ADA boost, Gradient Boost, XG boost
- Regularization L1 and L2 regularization
- Random Forest, Bagging
- Dimensionality Reduction with PCA
- Clustering and types
- Evaluate clustering results, Elbow Plot
- Train test split the data
- Kmeans Clustering
- Hierarchical clusterin
- Hyperparameter Tuning
- ML Project
- ML Project
- Stacking and Voting
- What is Deep Learning
- Hidden Layers
- Building project based on CNN
- Deep Learning Methods
- Activation Function
- Tensorflow, pytorch, Keras
- Deep Learning Application
- Forward and Backward propagation
- Batch Normalization, dropout
- Artificial Neural Network
- Deep Learning Libraries
- Performance measure for ANN
- CNN architecture
- CNN for computer vision
- Need for Hardwares in Deep learning
- Computer vision basics
- OpenCV
- Working with Images
- Edge detection
- Filtering
- Object detection
- Transfer Learning
- Pretrained models,
- Restnet50, Imagenet, Mobilenet
Relational Database – SQL – 10 Days
- Introduction to databases and RDBMS
- Read, update and delete operations on tables. Working with nulls
- Joins in SQL and working examples
- SQL set based operations and data aggregation, Sub-queries in SQL
- EBS (Elastic Block Storage),VPC
- Database creation, concept of relation and working examples
- Querying tables: Select statement, examples and its variations
- Insert, Update, Delete operations and working examples
- Normalization and de-normalization: Views and Temporary tables, Transactions in SQL
- EBS volumes and Snapshots
- Creating tables. Design view of the table, Alter table operations & Key Constraints
- Filtering, Sorting, Predicates and working examples
- Scalar functions in SQL and working examples
- SQL programming, Creating stored procedures, Cursors in SQL
- RDS
Python Programming – 10 Days
- Introduction to Python
- Python Functions
- Scope of Variables
- List and Tuple
- Map and filter functions
- Set and Dictionary
- Python Data types and Conditions
- Default arguments
- Global specifier
- List Methods
- String
- Exception Handling
- Control Statements
- Functions with variable number of args
- Working with multiple files
- List Comprehension
- List comprehension with conditionals
- File Handling
Exploratory Data Analysis with Pandas – 10 Days
- NumPy
- Slicing of Matrices
- NumPy Functions across axis
- Pandas Series
- Pandas Data Frame
- Working with Categorical Data
- Sorting Data Frames
- Creating graphs using Matplotlib
- Scatter Plot, Line Graph
- Seaborn
- Vectorization
- Filtering
- Stacking of arrays
- Data Cleaning
- Selection Data (loc, iloc)
- Grouping & Aggregation
- Importing csv files
- Customizing Plots
- Bar Graph, Histogram
- Matplotlib
- Broadcasting
- Array Creation Functions
- Matrix Calculation
- Handling Missing Data
- Filtering Data Frames
- Merging Data Frame (concat, merge)
- Importing Excel Files
- Saving Plots
- Subplots
Foundational Statistics 5 Days
- Logarithm
- Standard Deviation
- Descriptive and Inferential Statistics
- Mean, Median, Mode
- Percentile
- Log Normal Distribution
- Standardization and normalization
- Python Scipy Library
- Probability and Distribution
- Binomial Theorem
- Hypothesis testing
- Inferential Statistics
- Chi-square test, T test
- Ordinal, frequency encoding
- Mean Absolute Deviation
- Normal Distribution and Z Score
- Visualizing Data
- Variance: ANOVA
- Statistical Significance
- Data Preprocessing
- Transformation
Data Analysis and Visualization Using Tableau –7 Day
- Tableau Introduction
- Working with sets
- Connect Tableau with Different Data Sources
- Cards in Tableau
- Tableau Calculations using Functions
- Traditional Visualization vs Tableau
- Creating Groups
- Visual Analytics
- Charts, Dash-board
- Building Predictive Models
- Tableau Architecture
- Data types in Tableau
- Parameter Filters
- Joins and Data Blending
- Dynamic Dashboards and Stories
Data Science Specialization:
Foundational Machine learning – 7 Days
- Logarithm
- Standard Deviation
- Descriptive and Inferential Statistics
- Mean, Median, Mode
- Percentile
- Log Normal Distribution
- Mean Absolute Deviation
- Handson Examples
- Python Scipy Library
- Probability and Distribution
- Binomial Theorem
- Hypothesis testing
- Inferential Statistics
- Chi-square test, T test
- Ordinal, frequency encoding
- Data Preprocessing
- Handling missing data
- Onehot Encoding
- Label encoding
- Standardization and normalization
- Binning
- Transformation
- Case study: To perform Data cleaning and statistical analysis
Data Analysis and Visualization Using Excel –7 Day
- Introduction to excel
- Intro to Analyzing Data Using Spreadsheets
- Charting techniques in Excel
- Viewing, Entering, and Editing Data
- Converting Data with Value and Text
- Interactive dashboard creation
- Introduction to Data Quality
- Apply logical operations to data using IF
- Data analytics project using Excel
Advanced Python and Unit Testing- 10 Days
- Object Oriented Programming
- Regular Expression
- Testing Fundamentals
- Decorators
- Iterators
- Overloading Operator
- Finding Patterns of Text
- Unit Testing with Pytest
- UI Development with Tkinter
- UI development Mini Project
- Inheritance
- Meta characters
- Working with JSON
- Containers
Advanced Machine Learning and Model Improvement – 10Days
- Optimizing regression models with forward elimination, grid search cv
- Regularization L1 and L2 regularization
- Clustering and types
- Kmeans Clustering
- ML Project
- Improving classification models with Ensemble modeling
- Random Forest, Bagging
- Evaluate clustering results, Elbow Plot
- Hierarchical clustering
- SVM
- Boosting techniques,: ADA boost, Gradient Boost, XG boost
- Dimensionality Reduction with PCA
- Train test split the data
- Hyperparameter Tuning
- Stacking and Voting
Deep Learning using TensorFlow – 10Days
- What is Deep Learning
- Deep Learning Methods
- Deep Learning Application
- Artificial Neural Network
- CNN architecture
- Computer vision basics
- Edge detection
- Transfer Learning
- Hidden Layers
- Activation Function
- Forward and Backward propagation
- Deep Learning Libraries
- CNN for computer vision
- OpenCV
- Filtering
- Pretrained models
- Building project based on CNN
- Tensorflow, pytorch, Keras
- Batch Normalization, dropout
- Performance measure for ANN
- Need for Hardwares in Deep learning
- Working with Images
- Object detection
Capstone Project on using DL Methods : 5 Days
- Capstone Title Selection
- Project Basic model
- Project report Submission
- Abstract submission
- Interim Report
- Literature Survey
- Final Model Deployment with Pipeline
Placement Statistics
FAQs
Can I learn Data Science Online?
Yes, You can learn complete Data Science Course online.
Will I get a certificate after completion of the Data Science Course?
Yes, Certification of completion will be awarded after successful completion of all modules and clearing modules tests.
Does Data Science require coding?
Yes, Data science requires coding, required coding concepts will be covered during the course time, Python programming is used for the same.
Is doing Data Science worth it?
Yes, Data Science is in very high demand with an increase in data collection by every organization, and it is also one of the highest-paying jobs you can expect.
Who are eligible for Machine Learning?
Graduates from Science, all Engineering streams, Mtech, and MCA can apply for this course.
Is a PG diploma in Data Science and Machine Learning equivalent to a Masters?
Cranes curriculum provides Masters equivalent teaching content along with the projects. But it is not a master’s degree certificate.
Testimonials
Jagadeeshraju
On completing my B. Tech in Computer Science and Engineering and as a fresher it was so difficult to get a better career. So, I joined Cranes Varsity for PG Diploma in Data Science and it really helped me a lot. I have learned a lot from the institution. The trainers are very professional and have in-depth knowledge about the subjects. The placement team is also very cooperative and provided me with good opportunities. I got placed in Onward Technologies Thanks to Cranes Varsity for helping me to get a better job.
Tushar Mishra
I joined Cranes Varsity, in August 2021, after my engineering hoping to start my career in the Data Science domain. The trainers here are very supportive and have profound knowledge in modules like Basic and advanced python, DAV, ML, DL, Tableau, etc. They also evaluate student performance through mock tests and interviews. They provide a significant amount of placement opportunities in reputed companies. I am extremely grateful to the placement team, because of them I got placed in Kyndryl. If you are a fresher hoping to get a job in your favorite domain then the Cranes Varsity is the place for you.
Mubashir
I have completed my B.E in Mechatronics. As a fresher, it was very difficult to get a better career. So, I joined Cranes Varsity for doing Data Science Training Program and it has really helped me a lot. I learned a lot from Cranes Varsity. The trainers are very professional and have in-depth knowledge about the subjects. The placement team is also very cooperative and they provide a lot of opportunities. At last, I got placed in Tek systems Thanks to Cranes Varsity for helping me to get a better job.
Arjun E
I have completed B E in Mechatronics. I joined this institute in September 2021 for PG Diploma in Data Science in Cranes Varsity. The Trainers are good. Mock tests are regularly conducted to improve our technical and aptitude skills. They provide a many numbers of placement opportunities to all and they are very supportive and guide you for placement I got placed in TataElxsi. If we put in your right efforts in the training, you will get 100% placements.
Dhanush Kumar S
I joined Cranes Varsity for Data Science Certification Courses after my engineering. I am 2021 passed out and I learned to start from basic python to advance python, Data analytics and visualization, RDBMS, ML, Tableau, etc. They provide multiple placement opportunities to all and they are very supportive and guide you for placement. I got placed in EY a big4 company with a very good package for a fresher. So if u need to learn new things and need placement Cranes Varsity is the best place for you.
Nikhil Gaikar
I came to know about Cranes Varsity through Career labs as it approached my institute. Teaching in Cranes Varsity is good and for the Data Science Online Course, they covered modules like Python, Advanced Python, DAV, RDBMS, Tableau, Machine Learning and DL, Web technology, and Cloud Computing. Also, they provide many placement opportunities. They regularly have module and placement tests so that we can test our knowledge and do better in actual placements. I got placed in Genpact