• Overview of Python and its importance in Data Science
  • Setting up Python environment (Anaconda, Jupyter Notebook)
  • Basic Python syntax and data types
  • Introduction to NumPy for numerical computing
  • Introduction to Pandas library for data manipulation
  • Series and DataFrame objects in Pandas
  • Data cleaning and preprocessing techniques
  • Working with missing data and handling duplicates
  • Introduction to data visualization libraries: Matplotlib and Seaborn
  • Basic plots: line plots, scatter plots, bar plots
  • Advanced visualization techniques: histograms, box plots, heatmaps
  • Customizing plots and adding annotations
  • Descriptive statistics: mean, median, mode, variance, standard deviation
  • Probability distributions: normal, binomial, Poisson
  • Hypothesis testing: t-tests, chi-square tests, ANOVA
  • Introduction to Scikit-Learn library for machine learning
  • Supervised learning algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forests
  • Model evaluation techniques: Cross-validation, ROC curves, Confusion matrix
  • Overview of NLP and its applications
  • Text preprocessing techniques: tokenization, stemming, lemmatization
  • Sentiment analysis with NLTK and TextBlob
  • Introduction to spaCy for advanced NLP tasks
  • Overview of Deep Learning and neural networks
  • Introduction to TensorFlow library for Deep Learning
  • Building and training a simple neural network using TensorFlow
  • Introduction to Keras for building Deep Learning models
  • Capstone project: Applying Python and Data Science skills to solve a real-world problem
  • Data collection, preprocessing, modeling, and evaluation
  • Presentation of project results and findings
  1. Project 1 (Ex: Predictive Analytics for E-commerce Sales)
  2. Project 2 (Ex: Sentiment Analysis for Social Media Trends)
  1. Project 1 (Ex: Image Recognition for Autonomous Vehicles)
  2. Project 2:(Ex: Natural Language Processing for Chatbots)
  1. Project 1 (Ex: Customer Segmentation for Retail Industry)
  2. Project 2 (Ex: Fraud Detection in Financial Transactions)
  • Duration: 1 month
  • Cost: Rs.30,000

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