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
PG Diploma in Data Science and Machine Learning
Description
PG Diploma in Data Science
PG Diploma in data science and machine learning with placement guarantee is a comprehensive programs at Cranes Varsity which covers RDBMS, Python, DAV using Numpy & Pandas, Machine Learning, Deep Learning and Tableau.
An experienced person without Data Science knowledge can make a career as a Data Scientist. While having a strong foundation in Data Science concepts and techniques is advantageous, it is not always a prerequisite for starting a career as a Data Scientist. Â Â
After completion of the Data Science course, you will develop expertise in several key areas. Firstly, you will learn data manipulation and analysis techniques using programming languages 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.Â
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.Â
Our dedicated career development sessions, resume building assistance, and interview preparation help you to enhance your employability. Our strong industry connections and collaborations enable us to provide job placement assistance, connecting you with top companies in the automotive sector. We take pride in our high placement record and strive to help you kick-start a successful career in embedded and automotive systems.Â
Project stream:
- Apply statistical methods to make decision in various business problems, including bank, stock market 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.
- Gesture recognition captured through image or video data
Â
An experienced person without Data Science knowledge can make a career as a Data Scientist. While having a strong foundation in Data Science concepts and techniques is advantageous, it is not always a prerequisite for starting a career as a Data Scientist. Â
PG Diploma in data science and machine learning is comprehensive programs at Cranes Varsity which covers RDBMS, Python, DAV using Numpy & Pandas, Machine Learning, Deep Learning and Tableau.Â
After completion of the course, you will develop expertise in several key areas. Firstly, you will learn data manipulation and analysis techniques using programming languages 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.Â
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.Â
Our dedicated career development sessions, resume building assistance, and interview preparation help you to enhance your employability. Our strong industry connections and collaborations enable us to provide job placement assistance, connecting you with top companies in the automotive sector. We take pride in our high placement record and strive to help you kick-start a successful career in embedded and automotive systems.Â
The PG Diploma in Data Science 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.
PG Diploma in Data Science 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, and 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 Course with 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:
PG Diploma in Data Science Course Modules
Generic
- RDBMS using MySQL
- Python for Data Science
- Advanced Python (Testing and Web Scraping)
- Exploratory Data Analysis using Pandas
Data Science Specialization
- 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
Projects
- 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.
Platform
- Anaconda Distribution Jupyter, Spyder
- Tableau
- Google Colab
Course Content
- 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
- Importing Data in PowerBI
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Generic:
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:
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
Hiring Partners
FAQs
What are the career opportunities in Data Science?
Career opportunities are very high with high pay packages compared to other domains, Roles like Data scientist, Data analyst, business analyst, Data analytics manager, Business intelligence manager and many other roles can be expected.
What are the prerequisites to learn Data Science?
Having prior knowledge of any programming language is an added advantage but not mandatory, and the basics of linear algebra are a must.
What are the prerequisites for Machine Learning Course enrollment?
Having a good knowledge of Python Programming concepts, and a good hold on linear algebra.Â
Can I learn Machine Learning Online?
Yes, you can learn Machine Learning Course completely 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.
What courses does Cranes Varsity offer in Data Science?
Cranes Varsity offers a range of courses in Data Science, including:
• Post Graduate Diploma in Data Science
• Advanced Certificate in Data Analytics
• Short-term courses on Machine Learning, Deep Learning, and Data Visualization
• Corporate training programs for organizations looking to upskill their teams
Who can benefit from the Data Science courses at Cranes Varsity?
Our Data Science courses are designed for:
• Graduates in fields like Computer Science, Statistics, Mathematics, Engineering, and related areas.
• Working professionals looking to transition into data science roles or enhance their analytics skills.
• Business analysts seeking to deepen their understanding of data-driven decision-making.
• Research professionals wanting to apply data science techniques in their work.
What is the duration of Data Science courses at Cranes Varsity?
The duration of courses varies:
• Post Graduate Diploma in Data Science: 6 to 12 months.
• Advanced Certificate: 3 to 6 months.
• Short-term courses: 1 to 3 months, depending on the specific topic.
What are the eligibility criteria for enrolling in Data Science courses?
Eligibility criteria typically include:
• A bachelor’s degree in computer science, Statistics, Mathematics, or a related field.
• Basic programming knowledge in languages like Python or R is advantageous but not mandatory for beginners.
What skills will I learn from the Data Science courses?
By completing a Data Science course at Cranes Varsity, you will acquire skills such as:
• Data manipulation and cleaning using Python libraries (Pandas, NumPy).
• Statistical analysis and hypothesis testing.
• Machine learning algorithms include regression, classification, clustering, and deep learning.
• Data visualization techniques using tools like Matplotlib, Seaborn, and Tableau.
• Big data technologies like Hadoop and Spark.
What programming languages are taught in Data Science courses?
In our Data Science courses, you will primarily learn:
• Python: For data manipulation, analysis, and machine learning.
• R: For statistical analysis and data visualization.
• SQL: For database management and querying
What tools and software will I work with during the Data Science course?
Students will gain hands-on experience with various tools and software, including:
• Jupyter Notebook: For interactive coding and data analysis.
• Tableau: For data visualization and business intelligence.
• TensorFlow and Keras: For machine learning and deep learning projects.
• Apache Hadoop and Spark: For handling big data.
What types of projects can I expect to work on during the Data Science course?
Students will work on various projects, including:
• Analyzing and visualizing datasets using Python and R.
• Building machine learning models for predictive analytics.
• Implementing data pipelines for data collection and processing.
• Real-world case studies to solve business problems using data-driven insights
Does Cranes Varsity provide placement assistance for Data Science courses?
Yes, Cranes Varsity offers comprehensive placement support to students. We have connections with numerous tech companies and startups. Our placement assistance includes:
• Resume building and interview preparation.
• Access to job opportunities in data science, analytics, and related fields.
• Networking opportunities with industry professionals.
What job roles can I pursue after completing a Data Science course?
Upon completing a Data Science course at Cranes Varsity, you can pursue roles such as:
• Data Scientist
• Data Analyst
• Machine Learning Engineer
• Business Intelligence Analyst
• Statistician
What is the duration of PG Diploma in Data Science and Machine Learning?
Program duration is 6 Months.
What certificates will I receive on completion of the training program?
All students will get Successful Course Completion by Cranes Varsity.
How does Cranes Varsity prepare students for industry requirements in Data Science?
Our curriculum is designed in collaboration with industry experts to meet the latest trends and demands in the data science field. We emphasize:
• Practical training through projects and real-world applications.
• Industry-relevant case studies to develop problem-solving skills.
• Guest lectures and workshops from data science professionals.
Are the Data Science courses at Cranes Varsity more theoretical or practical?
Cranes Varsity emphasizes a hands-on learning approach. While theoretical concepts are crucial, a significant portion of the course focuses on practical lab sessions, projects, and applications of data science tools and techniques.
What are the assessments like in the Data Science courses?
Assessments in the Data Science courses include:
• Hands-on coding assignments and projects.
• Quizzes and exams to test theoretical understanding.
• Capstone projects where students apply their knowledge to solve real-world problems
Is there online support for the Data Science courses?
Yes, Cranes Varsity provides online learning support for Data Science courses, including:
• Access to recorded lectures and tutorials.
• Virtual labs for practical experimentation.
• Online assignments and assessments.
• Interactive Q&A sessions with faculty
How can I enroll in a Data Science course at Cranes Varsity?
To enroll in a Data Science course:
• You can fill out the application form and the dedicated admission counsellor will contact you..
• You can also visit our campus for direct inquiries and enrollment assistance.
Who are your trainers for the program and how are they selected?
Our trainers are seasoned professionals and industry experts with over 10+ years of relevant experience teaching the AI and ML certification course. Each of them has gone through a rigorous selection process that includes profile screening, and technical evaluation before they are certified to train for us. We also ensure that only trainers with a high alumni rating continue to train for us.
Are there scholarships or discounts available for Data Science courses?
Yes, Cranes Varsity offers scholarships and financial assistance for eligible students based on merit and need. We also provide early-bird discounts for those who enroll before specified deadlines.
Testimonials
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.
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.
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.
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.
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.
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
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Tushar Mishra
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Dhanush Kumar S
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