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- The Data Science Training Institute in Gurgaon is a premier training center that provides cutting-edge education in data science. Whether you are a fresh graduate or a working professional, this institute helps you enhance your skills and gain deep knowledge of data science concepts. Best Data Science Course in Gurgaon is designed to offer both practical and theoretical knowledge in areas like machine learning, data analysis, and artificial intelligence. In a city like Gurgaon, where tech companies are booming, this institute offers an excellent opportunity to become a well-rounded data science professional. The Data Scientist Course in Gurgaon will teach you how to analyze data and use it to make smart decisions.
Data Science Course in Gurgaon
- The primary objective of the Data Science Training in Gurgaon is to provide students with the knowledge and skills required to work as proficient data scientists. The Best Data Science Course in Gurgaon program aims to cover fundamental concepts along with hands-on practice using industry-standard tools. Students will gain a comprehensive understanding of:
- The Data Science Course in Gurgaon with Placement helps you gain the skills you need while also offering placement assistance to ensure you start your career on the right foot.
Core Data Science Concepts: Learn about data exploration, data cleaning, and preprocessing.
Statistical Methods & Machine Learning: Understanding the theory and application of algorithms, such as regression, clustering, and classification.
Big Data Analytics: Use tools like Hadoop and Spark to process large data sets.
Data Visualization: Learn how to present data effectively using tools like Tableau and Power BI.
Programming Languages: Master Python and R for data analysis and machine learning.
- After completing the course at the Data Science Training Institute in Gurgaon, freshers can expect a good salary package. Companies are actively seeking skilled data scientists, and the demand for these professionals is high.
Entry-Level Salary: Freshers can expect a salary between 4.5 Lakhs to 6 Lakhs per annum.
Mid-Level Salary: After gaining some experience (2-3 years), salaries can range from 8 Lakhs to 12 Lakhs per annum.
Senior-Level Salary: With 5+ years of experience, professionals can earn 15 Lakhs and above per annum.
- Data science professionals have excellent career growth opportunities. As you gain experience with Data Science Training in Gurgaon, your roles and responsibilities will evolve, allowing you to work on more advanced projects. Here's how your career could progress after completing the course:
- If you are looking to become a data scientist, the Data Scientist Course in Gurgaon is the perfect place to start your learning journey.
Junior Data Scientist: Start by working on smaller projects, cleaning and analyzing data.
Mid-Level Data Scientist: After gaining experience, you will handle more complex projects, build machine learning models, and lead teams.
Senior Data Scientist/Lead Data Scientist: Lead teams, strategize data-related projects, and contribute to high-level decision-making.
Specialized Roles: With additional expertise, you can move into specific areas like Artificial Intelligence (AI), Natural Language Processing (NLP), or Big Data.
- The Data Science Training Institute in Gurgaon stands out due to several key reasons:
Hands-on Training: Students get to work on real-world projects, ensuring they are job-ready.
Industry-Relevant Curriculum: The Data Science Course in Gurgaon is regularly updated to reflect the latest industry trends and tools.
Experienced Instructors: Trainers have practical experience working in the field, providing valuable insights to students.
Proximity to Tech Companies: Gurgaon is home to many multinational tech companies, offering abundant job opportunities for trained data scientists.
- The Data Science Course in Gurgaon course gives you a complete understanding of data science, from basics to advanced techniques, helping you become a skilled data scientist ready for real-world challenges. After completing the Data Science Training in Gurgaon, students can take on various roles in the data science field. These roles include:
- With the Data Science Course in Gurgaon with Placement, youll get the knowledge and hands-on experience needed, along with support to secure a job after completing the course.
Data Scientist: Analyzing large data sets, building machine learning models, and deriving insights to help businesses make informed decisions.
Data Analyst: Focuses on data collection, cleaning, and reporting to support business decisions.
Machine Learning Engineer: Implements algorithms and designs systems that automatically learn from data.
Data Engineer: Builds and optimizes systems for data collection, storage, and processing.
- Data Science Training in Gurgaon provides practical learning and teaches you how to use the latest tools and methods to solve problems, making you ready for a career in data science. The skills learned at the Data Science Training Institute in Gurgaon are in high demand across various industries. Some of the top industries hiring data scientists include:
Technology: Tech companies use data science to improve products and services.
Finance: Banks and financial institutions use data science for risk management, fraud detection, and market analysis.
Healthcare: Data scientists help analyze medical data, predict disease trends, and improve patient care.
Retail & E-commerce: Companies in this industry use data science for customer segmentation, sales forecasting, and personalized marketing.
Manufacturing: Data science is used to optimize supply chains, reduce costs, and predict maintenance issues.
- Upon completing the Data Science Training in Gurgaon, students receive official certifications that enhance their employability. These certifications validate the skills you have learned and can be added to your resume or LinkedIn profile.
- The Data Science Course in Gurgaon with Placement is a great way to learn data science and get a job offer through the institutes placement support.
Industry Recognition: Certification from a reputable institute adds credibility to your skills.
Job Readiness: The certification assures employers that you have the necessary skills to perform as a data scientist.
Continuous Learning: Certifications often serve as proof that you are committed to staying updated in your field.
- The cost of the Data Science Training Institute in Gurgaon is affordable and provides excellent value for the quality of education. The fee structure typically ranges from 35,000 to 70,000 depending on the course duration and additional certifications. Some options include:
- The Best Data Science Course in Gurgaon offers certifications from well-known platforms like Microsoft, IBM, and Coursera. These certifications are recognized in the industry and validate the skills you have gained throughout the course. The fee for these Data Science Course in Gurgaon is generally included in the course fee, though there may be additional charges for premium certifications.
Certification Fee: 5,000 to 10,000 (if required).
Global Recognition: These certifications help boost your resume and make you more attractive to employers.
- The Data Science Training Institute in Gurgaon covers a wide range of technical concepts and tools:
Programming Languages: Python, R, SQL.
Data Manipulation: Pandas, NumPy.
Machine Learning Algorithms: Regression, classification, clustering, decision trees, neural networks.
Big Data: Hadoop, Spark.
Data Visualization: Tableau, Power BI, Matplotlib, Seaborn.
Tools for Data Processing: TensorFlow, Scikit-learn.
- Best Data Science Course in Gurgaon offers real-time projects that allow students to apply their skills:
Customer Segmentation: Using clustering algorithms to divide customers into different groups based on purchasing behavior.
Stock Market Prediction: Applying machine learning to predict stock prices based on historical data.
Image Classification: Using neural networks for classifying images in various categories.
Sentiment Analysis: Analyzing customer reviews or social media posts to determine public sentiment using NLP.
- Students should choose this Best Data Science Course in Gurgaon because:
Comprehensive Curriculum: The course covers everything from basic data analysis to advanced machine learning techniques.
Industry-Standard Tools: Learn and master the tools and techniques used by data scientists in real companies.
Job Opportunities: Gurgaon is a hub for top tech companies, and trained data scientists are in high demand.
Hands-on Experience: Real-time projects provide students with practical skills that can be used immediately in the job market.
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CURRICULUM & PROJECTS
Data Science Training Program
- Introduction To Python
- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
- Version Control with Git & GitHub
- Python Keyword and Identiers
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
- Introduction To Variables
- Variables, expression condition and function
- Global and Local Variables in Python
- Packing and Unpacking Arguments
- Type Casting in Python
- Byte objects vs. string in Python
- Variable Scope
- Python Data Type
- Declaring and using Numeric data types
- Using string data type and string operations
- Understanding Non-numeric data types
- Understanding the concept of Casting and Boolean.
- Strings
- List
- Tuples
- Dictionary
- Sets
- Control Structure & Flow
- Statements if, else, elif
- How to use nested IF and Else in Python
- Loops
- Loops and Control Statements.
- Jumping Statements Break, Continue, pass
- Looping techniques in Python
- How to use Range function in Loop
- Programs for printing Patterns in Python
- How to use if and else with Loop
- Use of Switch Function in Loop
- Elegant way of Python Iteration
- Generator in Python
- How to use nested Loop in Python
- Use If and Else in for and While Loop
- Examples of Looping with Break and Continue Statement
- How to use IN or NOT IN keyword in Python Loop.
- Python Function, Modules and Packages
- Python Syntax
- Function Call
- Return Statement
- Arguments in a function Required, Default, Positional, Variable-length
- Write an Empty Function in Python pass statement.
- Lamda/ Anonymous Function
- *args and **kwargs
- Help function in Python
- Scope and Life Time of Variable in Python Function
- Nested Loop in Python Function
- Recursive Function and Its Advantage and Disadvantage
- Organizing python codes using functions
- Organizing python projects into modules
- Importing own module as well as external modules
- Understanding Packages
- Random functions in python
- Programming using functions, modules & external packages
- Map, Filter and Reduce function with Lambda Function
- More example of Python Function
- List
- What is List.
- List Creation
- List Length
- List Append
- List Insert
- List Remove
- List Append & Extend using + and Keyword
- List Delete
- List related Keyword in Python
- List Revers
- List Sorting
- List having Multiple Reference
- String Split to create a List
- List Indexing
- List Slicing
- List count and Looping
- List Comprehension and Nested Comprehension
- Tuple
- What is Tuple
- Tuple Creation
- Accessing Elements in Tuple
- Changing a Tuple
- Tuple Deletion
- Tuple Count
- Tuple Index
- Tuple Membership
- TupleBuilt in Function (Length, Sort)
- Dictionary
- Dict Creation
- Dict Access (Accessing Dict Values)
- Dict Get Method
- Dict Add or Modify Elements
- Dict Copy
- Dict From Keys.
- Dict Items
- Dict Keys (Updating, Removing and Iterating)
- Dict Values
- Dict Comprehension
- Default Dictionaries
- Ordered Dictionaries
- Looping Dictionaries
- Dict useful methods (Pop, Pop Item, Str , Update etc.)
- Sets
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
- Strings
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Python Exception Handling
- Python Errors and Built-in-Exceptions
- Exception handing Try, Except and Finally
- Catching Exceptions in Python
- Catching Specic Exception in Python
- Raising Exception
- Try and Finally
- Python File Handling
- Opening a File
- Python File Modes
- Closing File
- Writing to a File
- Reading from a File
- Renaming and Deleting Files in Python
- Python Directory and File Management
- List Directories and Files
- Making New Directory
- Changing Directory
- Python Database Interaction
- Basic SQL, DDL and DML commands
- SQL Database connection using
- Creating and searching tables
- Reading and Storing cong information on database
- Programming using database connections
- Reading an excel
- Working With Excel
- Reading an excel le using Python
- Writing to an excel sheet using Python
- Python| Reading an excel le
- Python | Writing an excel le
- Adjusting Rows and Column using Python
- ArithmeticOperation in Excel le.
- Play with Workbook, Sheets and Cells in Excel using Python
- Creating and Removing Sheets
- Formatting the Excel File Data
- More example of Python Function
- Complete Understanding of OS Module of Python
- Check Dirs. (exist or not)
- How to split path and extension
- How to get user prole detail
- Get the path of Desktop, Documents, Downloads etc.
- Handle the File System Organization using OS
- How to get any les and folders details using OS
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- Data Analysis and Visualization using Pandas.
- Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
- How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
- Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalues
- Exploring the Data Plotting, Correlations, and Histograms
- Getting statistical information about the data Analysis Concepts, Handle the None Values
- Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
- Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
- Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
- Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
- Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
- Exporting the results to Excel Joins
- Python | Pandas Data Frame Inner Join
- Under sting the properties of Data Frame Left Join (Left Outer Join)
- Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
- Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
- Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
- Pandas | How to Group Data How to apply Lambda / Function on Data Frame
- Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
- Data Analysis and Visualization using NumPy
- Introduction to NumPy Numerical Python
- Importing NumPy and Its Properties
- NumPy Arrays
- Creating an Array from a CSV
- Operations an Array from a CSV
- Operations with NumPy Arrays
- Two-Dimensional Array
- Selecting Elements from 1-D Array
- Selecting Elements from 2-D Array
- Logical Operation with Arrays
- Indexing NumPy elements using conditionals
- NumPys Mean and Axis
- NumPys Mode, Median and Sum Function
- NumPys Sort Function and More
- Data Analysis and Visualization using MatPlotLib
- Bar Chart using Python MatPlotLib
- Column Chart using Python MatPlotLib
- Pie Chart using Python MatPlotLib
- Area Chart using Python MatPlotLib
- Scatter Plot Chart using Python MatPlotLib
- Play with Charts Properties Using MatPlotLib
- Export the Chart as Image
- Understanding plt. subplots () notation
- Legend Alignment of Chart using MatPlotLib
- Create Charts as Image
- Other Useful Properties of Charts.
- Complete Understanding of Histograms
- Plotting Different Charts, Labels, and Labels Alignment etc.
- Introduction to Data Visualization with Seaborn
- Introduction to Seaborn
- Making a scatter plot with lists
- Making a count plot with a list
- Using Pandas with seaborn
- Tidy vs Untidy data
- Making a count plot with a Dataframe
- Adding a third variable with hue
- Hue and scattera plots
- Hue and count plots
- Visualizing Two Quantitative Variables
- Visualizing a Categorical and a Quantitative Variable
- Customizing Seaborn Plots
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- Foundation for AI: Learn traditional ML models, evaluation, and workflows.
- Introduction to ML, AI, and Deep Learning
- Types of ML (Supervised, Unsupervised, Reinforcement)
- ML Pipeline: Data Cleaning, Feature Engineering
- Common ML Algorithms: Linear, Logistic, DT, RF, SVM, KNN
- Model Evaluation: Accuracy, Precision, Recall, F1, ROC-AUC
- Overfitting, Underfitting, Cross-Validation
- Hands-on Project: Titanic Dataset (or similar)
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- Understand the inner workings of neural networks and train them with Keras.
- Introduction to Neural Networks & Deep Learning
- Activation Functions (ReLU, Sigmoid, Tanh)
- Feedforward Neural Network
- Backpropagation & Gradient Descent
- Learning Rate, Schedulers & Optimizers (SGD, Adam, RMSProp)
- Softmax, Cross-Entropy Loss
- Keras Basics: Sequential API & Functional API
- Fully Connected Layer Forward/Backward Pass
- Regularization Dropout, Batch Normalization
- Data Preprocessing & Data Augmentation
- Weight Initialization Strategies
- Babysitting Learning: Overfit detection, TensorBoard Monitoring
- Hands-on: MLP on MNIST / Tabular data (e.g. HR Analytics)
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- Master CNNs, object detection, segmentation, and deployment.
- Basics of Images, Image Preprocessing
- Convolution: 2D Conv, Forward & Backward
- Pooling, Padding, Stride, Transposed Conv
- CNN Architectures: LeNet, AlexNet, VGG, ResNet
- GPU vs CPU for DL
- Transfer Learning: Inception, MobileNet, fine-tuning
- Semantic Segmentation using UNet
- Object Detection YOLO, SSD, Region Proposal
- Bounding Box Regressor
- Siamese Networks for Similarity Search
- Hands-on:
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- Train text models from scratch and with BERT.
- Introduction to NLP and Use Cases
- Preprocessing: Tokenization, Lemmatization, Stopwords, Normalization
- Feature Extraction: BOW, TF-IDF, N-Grams
- Word Embeddings: Word2Vec, GloVe, Dense Vectors
- POS Tagging, Named Entity Recognition
- RNN, LSTM Forward Pass and BPTT
- Advanced LSTM Applications + Architectures
- Attention Mechanism + Encoder-Decoder
- Transformers, BERT, Hugging Face Pipelines
- NLP Evaluation Metrics: BLEU, ROUGE
- Hands-on:
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- Take models from notebooks to real-world applications.
- Saving & Loading Models (Pickle, Joblib, Keras)
- Flask vs FastAPI Serving ML models
- Streamlit/Gradio for Web Apps
- Hosting Models on Hugging Face Spaces, Streamlit Cloud
- MLflow Intro Model Tracking & Versioning
- Hands-on:
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- Build, evaluate, and deploy a mini AI project end-to-end.
- Project Selection: Tabular, CV, or NLP
- Data Collection/Exploration
- Preprocessing + Feature Engineering
- Model Training & Tuning
- Evaluation & Interpretation
- App Creation (Streamlit/Gradio)
- Deployment + Final Presentation/Submission
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Course Duration
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FAQ's
In addition to recorded video courses of lectures, you will get the opportunity to work on actual projects with industry specialists that have more than 10 years of experience at the Data Science Training Institute in Gurgaon.
The well-equipped laboratories and the actual project would substantially help the Data Science Course in Gurgaon. You'll receive more real-world experience as a consequence, and you'll be better equipped to find a great job.
Croma Campus provides Data Science Training in Gurgaon with Certification for you to join in so that you may gain complete skills. The institute's high-quality training will provide you with a diverse set of skills and knowledge.
Because the Data Science Training in Gurgaon is customizable, you may do it on your own schedule. However, it will take 30 hours to complete.
To enroll in the Croma Campus Data Science Training Institute in Gurgaon, go to helpdesk@cromacampus.com. You may reach us by WhatsApp (+91-9818014543) or phone (+91-9711526942).
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