Master Data Science with Python

About The Course

The volume of data increases day by day, and it’s a gold mine. With Data and mining techniques, a business can know a lot about the world and reap huge profits. For reaping those profits, miners called Data Scientists and huge data processing experts called Data Engineers are needed.

The course we offer helps you to master data science. It’s 1:1 course, that is, you will be taught privately by an industry-leading Data Scientist and Data Engineer. You will have hands on to-do exercises which you need to complete, this will help you to attain mastery. You will be aided and guided in every step so that all difficulties will be abolished, and you be confident in every step.

Our mission is to create world-class data scientists, and we know you want to be one.

Timings

This course is designed to be flexible, you can choose any time of the day. This course is modelled to fit professionals and students alike. One can get up to 3 – 4.5 hours of classes per week, and in a day one can get 1 hour of coaching.

Duration

60 Classes are recommeded, and we expect one to complete this course in 6 months.

Syllabus

1. Introduction to Data Science and Python

  • Overview of Data Science:
    • What is Data Science?
    • Key skills in Data Science
    • Applications of Data Science in different industries (healthcare, finance, marketing, etc.)
  • Getting Started with Python:
    • Python installation and setup (Anaconda, Jupyter Notebook, etc.)
    • Python IDEs (VS Code, PyCharm, etc.)
    • Basic Python syntax, data types, and variables
    • Control flow: loops, conditionals
    • Functions and modules
    • Introduction to libraries: NumPy, Pandas, Matplotlib, Seaborn

2. Data Wrangling and Exploration

  • Working with Data Structures:
    • Lists, tuples, sets, and dictionaries
    • Introduction to NumPy arrays
    • Pandas DataFrames: creation, indexing, slicing, and selecting
  • Data Loading and Preprocessing:
    • Reading data from CSV, Excel, SQL, JSON, and APIs
    • Data Cleaning (handling missing values, duplicates)
    • Data transformation (renaming columns, changing data types, etc.)
  • Exploratory Data Analysis (EDA):
    • Descriptive statistics: mean, median, mode, standard deviation
    • Distribution analysis: histograms, boxplots
    • Correlation analysis: heatmaps, pairplots
    • Grouping data and aggregation using groupby()
    • Handling categorical data (encoding, one-hot encoding)

3. Data Visualization

  • Matplotlib Basics:
    • Line plots, scatter plots, bar plots, histograms
    • Customizing plots: titles, labels, legends, colors
    • Subplots and multiple visualizations
  • Advanced Visualization with Seaborn:
    • Distribution plots: KDE, boxplots
    • Categorical plots: countplot, barplot, violinplot
    • Heatmaps, pairplots, and facet grids
  • Interactive Visualizations:
    • Plotly for interactive plotting
    • Dash for building data dashboards

4. Statistical Analysis

  • Basic Probability and Statistics:
    • Probability distributions (normal, binomial, Poisson)
    • Hypothesis testing (t-tests, chi-squared test)
    • Confidence intervals and p-values
  • Correlation and Causality:
    • Pearson, Spearman, and Kendall correlation
    • Causal inference and confounding variables
  • Sampling Techniques:
    • Random sampling, stratified sampling
    • Bootstrapping, cross-validation

5. Introduction to Machine Learning

  • Supervised Learning:
    • Overview of supervised learning: classification vs regression
    • Regression Algorithms: Linear Regression, Polynomial Regression
    • Classification Algorithms: Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, Random Forest
  • Model Evaluation and Tuning:
    • Train-test split and cross-validation
    • Evaluation metrics: Accuracy, Precision, Recall, F1-score, ROC curve, AUC
    • Hyperparameter tuning using GridSearchCV and RandomizedSearchCV
  • Unsupervised Learning:
    • Overview of unsupervised learning
    • Clustering algorithms: K-Means, Hierarchical Clustering, DBSCAN
    • Dimensionality reduction: PCA (Principal Component Analysis)

6. Deep Learning and Neural Networks (Optional, Advanced)

  • Introduction to Deep Learning:
    • Neural networks basics: neurons, layers, activation functions
    • Architecture of a neural network
    • Gradient descent and backpropagation
  • Using Keras/TensorFlow/PyTorch:
    • Building simple neural networks in Keras
    • Implementing CNNs (Convolutional Neural Networks)
    • Introduction to Recurrent Neural Networks (RNNs)

7. Working with Time Series Data

  • Time Series Basics:
    • Time series data structure: dates, periods, frequency
    • Time-based indexing in Pandas
    • Handling missing data and outliers in time series
  • Forecasting Models:
    • ARIMA, SARIMA
    • Exponential smoothing (Holt-Winters)
    • Advanced forecasting models: Prophet

8. Natural Language Processing (NLP) (Optional, Advanced)

  • Text Preprocessing:
    • Tokenization, stemming, lemmatization
    • Removing stopwords, special characters, and non-alphanumeric text
    • Vectorization techniques: Bag of Words, TF-IDF
  • NLP Models:
    • Word embeddings: Word2Vec, GloVe
    • Sentiment analysis, text classification
    • Named Entity Recognition (NER), part-of-speech tagging

9. Big Data Tools and Technologies (Optional, Advanced)

  • Working with Large Datasets:
    • Introduction to big data and Spark
    • Using PySpark for distributed data processing
    • Introduction to Hadoop and MapReduce

10. Deployment and Production

  • Model Deployment:
    • Introduction to Flask or FastAPI for creating APIs
    • Dockerizing a model for deployment
    • Deploying on cloud platforms (AWS, GCP, Heroku)
  • Model Monitoring and Maintenance:
    • Model drift and re-training
    • Monitoring performance in production

11. Final Projects and Case Studies

  • End-to-End Projects:
    • Real-world data science project (from data collection to model deployment)
    • Examples: Predicting house prices, customer segmentation, fraud detection, etc.
    • Model deployment with web apps (Flask/Dash)
    • Case studies: Projects from Kaggle, UCI Machine Learning Repository, etc.

Recommended Books

  • Python Machine Learning - Sebastian Raschka
  • Python Data Science Handbook - Jake VanderPlas
  • Python for Data Analysis - Wes McKinney
  • Python for Data Science - Wes McKinney
  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow - Aurélien Géron
  • Deep Learning with Python - Francois Chollet
  • Deep Learning with Pytorch - Luca Antiga, Eli Stevens, Howard Huang, Thomas Viehmann

Fee Structure

The course costs Rs 45,000/-, initial one time payment. Rs 15,000/- should be paid after completion of course to get examination and certification.

Contact

For any queries, contact Karthikeyan +91 8428 05 0777