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Data Science with Python Shadow

Data Science with Python

Comprehensive Data Science with Python Course

Join Codeit's comprehensive "Data Science with Python"

Duration: 1 month
Fee: Rs.2499 /- Rs.30000

Course Overview: 

Data science is the multidisciplinary field focusing on extracting the insights with various combinations of techniques. At CodeIT, it offers a comprehensive courses in data science which is designed to empower the skills that are needed to excel, it combines techniques from computer science, statistics, mathematics, and domain expertise to analyze, process, and interpret data to make informed decisions and solve complex problems. You'll be able to learn essential tools like python, along with the libraries like Pandas, NumPY. CodeIT will guide you in mastering data visualization. 

Why choose Code IT?

Benefits

At CodeIT learning data science can give a numerous benefits to significantly boost your career and decision making and problem solving capabilities. 

  • Beginner friendly courses
  • Affordable: CodeIT offers its training in most affordable prices where  student can learn without hampering with their financial barriers
  • Career shifting: anyone looking for switching their career from non it background to it background.
  • Certification and life time support: after completion of the course youll be guided to make the real time projects that might be accepted by real business as well as youll get the free certificates along with the recorded videos of your online classes for the life time.
  • Hands on experience: youll gain the real practical knowledge, and learn about machine learning, AI. 

Who can learn this course?

CodeIT's  Data Science course is designed to be assessable to different learners from beginners to the advanced. It's suitable for anyone looking to enhance their knowledge

  • Beginners: if youre interested in AI and kickstart your career in data science
  • Career shifting: professionals from non-technical backgrounds looking to switch careers to a high-demand, rewarding field will find our structured and beginner-friendly approach helpful
  • Business owners: understanding Data Science can help you make data-driven decisions, optimize processes, and gain insights to grow your business
  • Tech Enthusiasts: Anyone passionate about data, statistics, or programming who wants to explore new skills and projects will find our courses engaging and fulfilling.
Materials included
Life time video access
Free Certificate
Future Support
Live sessions on Google Meet
Requirements
Laptop
Course Syllabus

Introduction to NumPy

  • Overview of arrays and their importance in data science
  • Creating arrays, reshaping, indexing, and slicing
  • Basic mathematical operations on arrays

Advanced NumPy Operations

  • Broadcasting in NumPy
  • Working with mathematical functions (mean, std, etc.).
  • Stacking, splitting, and performing matrix operations

Introduction to Pandas

  • Understanding Series and DataFrames
  • Reading and writing data (CSV, Excel).
  • Basic operations: indexing, filtering, and sorting

Data Cleaning with Pandas

  • Handling missing data (fillna, dropna).
  • Renaming, adding, and dropping columns
  • Replacing and transforming data

Aggregation and Grouping in Pandas Grouping data using groupby

  • Applying aggregation functions (sum, mean, etc.)
  • Pivot tables and multi-level indexing

Joining and Merging Data

  • Combining datasets using merge and concat
  • Handling hierarchical data
  • Merging with keys and indices

Mini Project 1: EDA and Data Cleaning

  • Project: Analyze a real-world dataset (e.g., Titanic or Sales Data).
  • Load, clean, and preprocess the data
  • Perform exploratory analysis (summary statistics, identifying trends).
  • Present insights visually using Pandas plots

Introduction to Data Visualization

  • Basics of visualization: Why and when to use plots?
  • Introduction to Matplotlib.
  • Creating line, bar, and scatter plots.

Advanced Visualization with Matplotlib

  • Customizing plots (titles, labels, legends, grid).
  • Subplots and layouts
  • Saving and exporting visualizations

Introduction to Seabom

  • Overview of Seaborn's capabilities
  • Visualizing distributions (histograms, KDE, box plots).
  • Creating pair plots and heatmaps.

EDA Techniques

  • Identifying outliers using visualizations
  • Understanding relationships using scatter matrices
  • Correlation analysis and visualization (heatmaps)

Handling Time Series Data

  • Introduction to time series data
  • Resampling, shifting, and rolling windows
  • Visualizing time trends

Real-world EDA Techniques

  • Case studies of EDA (e.g., sales data, customer data).
  • Hands-on practice with datasets.

Kaggle Competition 1

  • A beginner-friendly Kaggle competition based on EDA and data visualization
  • Students will explore and analyze a dataset provided On Kaggle.

Understanding Machine Learning Basics

  • What is Machine Learning?
  • Types of ML: Supervised, Unsupervised
  • ML Workflow: Data preparation, model building, evaluation.

Introduction to Linear Regression

  • The concept of regression and best-fit line.
  • Hands-on implementation using Scikit-learn.
  • Evaluating performance: RMSE, R2

Multiple Linear Regression

  • Extending linear regression to multiple features
  • Feature engineering basics
  • Evaluating multi-feature models

Introduction to Classification with Logistic Regression

  • What is classification?
  • Binary classification using logistic regression
  • Performance metrics: Accuracy, precision, recall, Fl -score

Decision Trees

  • Building and visualizing decision trees
  • Avoiding overfitting using pruning

Poject & Kaggle Competition 2

  • A Kaggle competition focusing on regression or classification tasks (e.g., predicting house prices or loan eligibil'ty).

Introduction to k-Nearest Neighbors (kNN)

  • Overview of the k-Nearest Neighbors (kNN) algorithm
  • Understanding the concept Of distance metrics (Euclidean, Manhattan)
  • Hands-on: Implementing kNN for classification tasks using Scikit-learn

Introduction to Clustering with K-Means

  • What is clustering and how does it differ from classification?
  • K-Means algorithm. Understanding the concept of centroids and clustering
  • Hands-on: Implementing K-Maans clustering on a dataset.

Introduction to Ensemble Learning Boosting

  • Overview of ensemble leaming methods (Bagging vs Boosting)
  • Introduction to popular boosting algorithms: AdaB00St and Gradient Boosting
  • Hands-on: Implementing AdaB00St and Gradient Boosting using Scikit-learn.

Neural Networks Basics (ANNS)

  • understanding artificial neural networks (ANNs) and how they function.
  • Exploring the architecture of an ANN: input layer, hidden layers, activation functions
  • Hands-on: Building a basic ANN using Keras/TensorFlow for binary classification
  • Backpropagation loss.

Capstone Mini Project - Data Preprocessing and Model Building

  • Clean and preprocess the data (handling missing values, feature engineering)
  • Implement a classification or regression model.
  • Evaluate the model's performance and optimize using hyperparameter tuning.

Kaggle Competition 3 - Real-World Application

  • Participate in a Kaggle competition focused On using the techniques learned throughout the week.
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