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Master all data science concepts by enrolling in our Data Science course at a leading Data Science Institute in Chandigarh.

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Course Duration

100 Hrs.

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5 Project

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  • Data Science training in Chandigarh provides essential skills and knowledge for aspiring data scientists, covering machine learning, statistical analysis, data visualization, and programming in Python and R.
  • A Data Science course in Chandigarh offers a blend of theoretical understanding and practical experience. The curriculum is designed to prepare participants for roles like data analysts, data engineers, and data scientists, with a focus on solving real-world problems and making data-driven decisions.
  • Attending a top Data Science institute in Chandigarh gives access to expert faculty, state-of-the-art resources, and the latest industry practices. These institutes emphasize a balanced approach of classroom instruction and hands-on projects, helping students build a strong portfolio.
  • Enrolling in Data Science training in Chandigarh connects students to a growing tech community and a robust job market, providing numerous career opportunities. The comprehensive education and practical experience gained from a reputable Data Science institute in Chandigarh significantly enhance career prospects in the data science field.

Data Science Course in Chandigarh

About-Us-Course

  • Data Science training in Chandigarh aims to develop highly skilled data professionals through a focused curriculum. The key objectives of this training are:
    • Core Knowledge Acquisition: Impart a thorough understanding of essential data science concepts such as machine learning, data mining, and big data technologies.

      Programming Mastery: Ensure proficiency in data science programming languages like Python and R, crucial for data manipulation and analysis.

      Practical Skills Development: Emphasize hands-on learning through real-world projects and case studies, fostering practical experience.

      Analytical Competence: Enhance participants' ability to analyse complex datasets and derive actionable insights.

      Industry Alignment: Tailor the curriculum to meet current industry demands, making graduates ready for immediate employment.

      Problem-Solving Aptitude: Strengthen the ability to tackle data-driven problems and devise innovative solutions.

      Certification Readiness: Equip participants with the knowledge and skills needed to successfully pass advanced data science certification exams.

      Career Enhancement: Improve career prospects by training participants for high-demand roles such as data scientist, data analyst, and data engineer.

  • By focusing on these objectives, Data Science training in Chandigarh prepares individuals to excel in various data science roles across multiple industries.

  • Completing Data Science training in Chandigarh can significantly boost your earning potential. Here are typical salary ranges for various roles:
    • Data Analyst: INR 3 to 5 lakhs per annum for entry-level; INR 6 to 8 lakhs per annum with experience.

      Data Scientist: INR 6 to 8 lakhs per annum for entry-level; INR 10 to 15 lakhs per annum for experienced professionals.

      Data Engineer: INR 4 to 6 lakhs per annum for entry-level; INR 8 to 12 lakhs per annum for experienced IT professionals.

      Machine Learning Engineer: INR 5 to 7 lakhs per annum for entry-level; INR 10 to 18 lakhs per annum with experience.

      Business Intelligence Analyst: INR 3 to 5 lakhs per annum for entry-level; INR 6 to 10 lakhs per annum with experience.

  • These salaries can vary based on factors like prior experience, employer, and industry, making Data Science training in Chandigarh a valuable investment for career growth.

  • Completing Data Science coaching in Chandigarh greatly improves career prospects by providing essential skills and numerous opportunities:
    • Open to roles like data analyst, data scientist, and machine learning engineer.

      Competitive salaries for both beginners and experienced professionals.

      Master data analysis, machine learning, and data visualization.

      Job opportunities in IT, finance, healthcare, retail, and more.

      Equipped for advanced data science certifications.

      Pathway to senior roles like data science manager or chief data officer.

  • A Data Science Online Course is popular because the Country is becoming a major tech centre, leading to a high demand for data professionals. Chandigarh has many good institutes that offer detailed courses, teaching important skills like data analysis, machine learning, and data visualization.
  • The growing job market in Chandigarh offers great career opportunities in various fields such as IT, finance, healthcare, and retail. Additionally, the attractive salaries for data science jobs make these courses even more appealing.

  • Upon completing a Data Science course in Chandigarh, you can expect to take on various roles and responsibilities, including:
    • Collect and clean large datasets

      Analyse data to find trends and patterns

      Develop predictive models and machine learning algorithms

      Create dashboards and reports to visualize data

      Collaborate with stakeholders to understand data needs

      Monitor and refine models for optimal performance

      Document methodologies and results

      Stay updated with the latest data science techniques

      Ensure data privacy and compliance

      Mentor and train junior data scientists

  • Enrolling in a Data Science course in Chandigarh at a top Data Science institute in Chandigarh can significantly advance your career. Here are the main industries seeking data scientists in Chandigarh:
    • Information Technology (IT) - Emphasizing data-driven decision-making.

      E-commerce - Enhancing supply chain efficiency and customer personalization.

      Healthcare - Advancing diagnostic procedures and healthcare management.

      Finance and Banking - Implementing fraud prevention and predictive modeling.

      Education - Developing personalized learning paths and improving academic outcomes.

      Telecommunications - Optimizing customer data insights and network performance.

      Manufacturing - Ensuring predictive upkeep and optimizing production lines.

      Retail - Gaining insights into consumer preferences and stock control.

      Travel and Tourism - Refining customer journeys and optimizing pricing models.

      Real Estate - Conducting market trend analysis and property valuation forecasts.

  • Upon finishing the Data Science Certification Course, you will earn a certificate of completion. This intensive program includes vital areas of Data Science, such as:
    • Machine Learning

      Statistical Analysis

      Data Visualization

      Programming in Python/R

  • The certificate confirms your proficiency in these subjects, showcasing your ability to work with data effectively for strategic insights. This globally acknowledged certificate is a mark of your expertise in Data Science. Additionally, it enables you to apply for relevant Data Science certification exams, enhancing your professional qualifications and career opportunities.

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CURRICULUM & PROJECTS

Data Science Training Program

    NA

    • 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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    NA

    • 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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    NA

    • 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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    NA

    • 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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    NA

    • 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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    NA

    • 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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    NA

    • 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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    NA

    • 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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FAQ's

Typically ranges from 3 to 6 months.

Machine learning, data visualization, statistical analysis, and Python/R programming.

Consider faculty expertise, course content, and alumni reviews.

High demand in IT, finance, healthcare, and e-commerce industries.

Basic knowledge of programming and statistics is beneficial but not mandatory.

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