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  • Data Science training in Lucknow offers a great opportunity for those looking to start or advance their career in data science. A Data Science course in Lucknow covers essential topics like data analysis, machine learning, statistics, and data visualization.
  • Benefits of enrolling in a Data Science institute in Lucknow:
    • Expert Instructors: Learn from experienced professionals.

      Hands-on Training: Work on real-world projects.

      Comprehensive Curriculum: Gain knowledge in all key areas of data science.

      Networking Opportunities: Connect with industry professionals and peers.

      Career Support: Receive guidance and assistance in job placements.

  • Data Science in Lucknow is becoming increasingly popular due to the city's growing tech industry and the high demand for skilled data scientists. With excellent training and support, Lucknow is a great place to build your data science career. Whether you're just starting out or looking to enhance your skills, data science training in Lucknow can help you achieve your goals.

Data Science Course in Lucknow

About-Us-Course

  • The goals of Data Science training in Lucknow are:
    • Foundation Knowledge: Grasp key concepts in statistics, machine learning, and data analysis.

      Hands-On Learning: Engage in practical projects and real-world applications.

      Tool Mastery: Gain proficiency in Python, R, SQL, and Tableau.

      Visualization Skills: Develop the ability to create impactful data visualizations.

      Analytical Thinking: Enhance skills in problem-solving and data interpretation.

      Job Preparedness: Get ready for industry demands and various career opportunities.

      Ongoing Education: Keep up with the latest developments in data science.

      Collaboration: Improve teamwork and communication abilities.

      Ethical Awareness: Learn best practices for ethical data management.

      Career Progression: Equip yourself for advanced roles like Data Scientist and Data Analyst.

  • These objectives ensure that students enrolled in a Data Science course in Lucknow at a leading Data Science institute in Lucknow are well-equipped for a successful career in data science.

  • After completing a Data Science course in Lucknow with placement, you can look forward to competitive salary packages:
    • Entry-Level: Fresh graduates can expect salaries ranging from 4 to 7 lakhs per annum.

      Mid-Level: Professionals with 2-5 years of experience can earn between 7 to 12 lakhs per annum.

      Senior-Level: Those with over 5 years of experience can see salaries from 12 to 20 lakhs per annum.

  • Choosing the best Data Science course in Lucknow enhances your career prospects and earning potential. These courses provide comprehensive training, hands-on projects, and industry exposure, making you job-ready.
  • Pursuing Data Science in Lucknow is a smart choice due to the citys growing tech industry and demand for skilled professionals. Graduates from a top Data Science institute in Lucknow are well-equipped with the latest tools and techniques, ensuring they stand out in the job market. Overall, completing a data science course in Lucknow can lead to a rewarding career with excellent salary prospects.

  • Learning Data Science in Lucknow offers numerous career benefits:
    • Enhanced Skills: Master tools like Python, R, SQL, and Tableau.

      Practical Experience: Work on real-world projects.

      Industry Readiness: Prepare for roles in IT, finance, healthcare, and e-commerce.

      Higher Salaries: Enjoy competitive pay with potential for growth.

      Networking: Connect with industry professionals and peers.

      Career Advancement: Move up to roles like Data Scientist, Data Analyst, and Machine Learning Engineer.

      Job Security: High demand for data science skills ensures job stability.

  • Completing a Data Science course in Lucknow from a top institute boosts your skills and employability, paving the way for a successful career.

  • A Data Science Online Course is popular due to following reasons:
    • High Demand: Growing need for data scientists across industries.

      Lucrative Salaries: Competitive pay and substantial growth potential.

      Versatile Skills: Applicable in IT, finance, healthcare, and e-commerce.

      Advanced Problem-Solving: Gain analytical and problem-solving skills.

      Cutting-Edge Technology: Involvement with machine learning and AI.

      Career Advancement: Opens up advanced roles and opportunities.

      Global Job Prospects: High demand worldwide.

      Continuous Learning: Constantly evolving field with new trends.

  • These factors make data science courses highly attractive to professionals.

  • After completing a Data Science course in Lucknow, you can expect to take on various roles and responsibilities, including:
    • Data Collection: Gather and organize data from various sources.

      Data Cleaning: Ensure data accuracy and quality by cleaning and preprocessing data.

      Data Analysis: Analyse large datasets to extract meaningful insights and trends.

      Model Building: Develop, test, and deploy machine learning models to solve business problems.

      Data Visualization: Create visualizations and dashboards to present data findings clearly and effectively.

      Statistical Analysis: Apply statistical techniques to interpret data and draw conclusions.

      Collaboration: Work with cross-functional teams, including IT, marketing, and management, to integrate data-driven solutions.

      Reporting: Prepare detailed reports and presentations to communicate insights and recommendations to stakeholders.

      Tool Utilization: Use data science tools and programming languages such as Python, R, SQL, and Tableau.

      Problem-Solving: Identify and address business challenges using data-driven approaches.

      Continuous Learning: Stay updated with the latest trends and advancements in data science and machine learning.

  • These responsibilities prepare you to excel in various data science roles across different industries.

  • Lucknow's growing tech and business environment offers diverse opportunities for data science professionals. Here are some top industries hire data scientists in Lucknow.
    • IT and Software: Work on innovative solutions and software improvements.

      Finance and Banking: Focus on risk management, fraud detection, and investment strategies.

      Healthcare: Enhance patient care, manage medical records, and develop new treatments.

      E-commerce: Optimize supply chains, improve customer experience, and tailor marketing efforts.

      Manufacturing: Engage in predictive maintenance, quality control, and production efficiency.

      Telecommunications: Improve network performance, analyze customer data, and enhance services.

      Retail: Manage inventory, forecast sales, and understand consumer behavior.

      Pharmaceuticals: Analyze clinical trials and support drug development processes.

      Energy and Utilities: Boost efficiency, manage resources, and implement smart grid technologies.

      Logistics and Supply Chain: Optimize routing, forecast demand, and manage inventory efficiently.

  • Once you are done with Data Science Certification Course, you will get a training certificate valid worldwide and increase your chances of getting hired by leading industries globally at attractive salary packages.

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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

You will learn data analysis, machine learning, statistics, and data visualization.

Enrolling in a Data Science course in Lucknow offers comprehensive training, experienced instructors, and practical projects.

The best Data Science institute in Lucknow, Croma Campus provides quality education, hands-on experience, and strong industry connections.

After completing Data Science in Lucknow, you can expect global job opportunities with attractive salary packages in various industries.

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