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    Data Science Projects with Python

    Python Programming Course
    The ``Data Science Projects with Python`` course aims to familiarize you with the Python environment tailored for data science tasks. It serves as a stepping stone in your quest to become proficient in various machine learning topics. These acquired skills will enable you to develop advanced predictive models, aligning with industry standards and delivering substantial value to businesses across diverse sectors.

    ``Data Science Projects with Python`` is meticulously crafted to provide practical guidance on utilizing industry-standard data analysis and machine learning tools in Python, utilizing real-world datasets. This course empowers you to grasp how to leverage pandas and Matplotlib to meticulously examine datasets through summary statistics and visualizations, extracting the desired insights effectively.

    Continuing your learning journey, you will delve into data preparation techniques and the application of machine learning algorithms, such as regularized logistic regression and random forest, utilizing the scikit-learn package. You will explore techniques for fine-tuning these algorithms to yield optimal predictions on new and unseen data instances.

    Advancing through subsequent chapters, you will gain a comprehensive understanding of the inner workings and outputs of these algorithms. This knowledge will not only enhance your predictive modeling capabilities but also enable you to comprehend the rationale behind these predictions.

    Upon completing this course, you will possess the skills necessary to proficiently employ various machine learning algorithms for in-depth data analysis and extract actionable insights from datasets with confidence.

    Hands on Training

    Data Science Projects with Python takes a case study approach to simulate the working conditions you will experience when applying data science and machine learning concepts. You will be presented with a problem and a data set and walked through the steps of defining an answerable question, deciding what analysis methods to use, and implementing all of this in Python to create a deliverable.

    Data Science Projects with Python Training Objectives

    • By the end of this course
    • you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data.

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    We have different work process to go step by step for complete our working process in effective way.
    • Data Science Projects with Python Training Prerequisites

      If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyse data and predict outcomes, this course is for you.

      Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful.

    • Data Science Projects with Python Training Format

      In-Person

      Online

    • Data Science Projects with Python Outline

      Lesson 1: Data Exploration and Cleaning

      Python and the Anaconda Package Management System
      Different Types of Data Science Problems
      Loading the Case Study Data with Jupyter and pandas
      Data Quality Assurance and Exploration
      Exploring the Financial History Features in the Dataset
      Activity 1: Exploring Remaining Financial Features in the Dataset

      Lesson 2: Introduction to Scikit-Learn and Model Evaluation

      Introduction
      Model Performance Metrics for Binary Classification
      Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve

      Lesson 3: Details of Logistic Regression and Feature Exploration

      Introduction
      Examining the Relationships between Features and the Response
      Univariate Feature Selection: What It Does and Doesn't Do
      Building Cloud-Native Applications
      Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients

      Lesson 4: The Bias-Variance Trade-off

      Introduction
      Estimating the Coefficients and Intercepts of Logistic Regression
      Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters
      Activity 4: Cross-Validation and Feature Engineering with the Case Study Data

      Lesson 5: Decision Trees and Random Forests

      Introduction
      Decision trees
      Random Forests: Ensembles of Decision Trees
      Activity 5: Cross-Validation Grid Search with Random Forest

      Lesson 6: Imputation of Missing Data, Financial Analysis, and Delivery to Client

      Introduction
      Review of Modeling Results
      Dealing with Missing Data: Imputation Strategies
      Activity 6: Deriving Financial Insights
      Final Thoughts on Delivering the Predictive Model to the Client