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Data Analytics with Python libraries

Data Analytics with Python libraries

course-meta
Instructors
Saleem

3x per week

45 sessions

session duration

60

Typical class size

20(Students)

Description

Data analytics involves the exploration, interpretation, and communication of meaningful patterns within datasets to aid decision-making and solve problems. Through statistical analysis, data visualization, and machine learning techniques, data analysts extract valuable insights from large volumes of data. These insights can inform strategic business decisions, optimize processes, and identify opportunities for growth or improvement. Data analytics plays a crucial role across various industries, empowering organizations to make data-driven decisions and stay competitive in today's rapidly evolving landscape.

What you'll learn

    1. Data Acquisition and Preprocessing:

    •    - File Handling: Reading and writing data from/to various file formats (CSV, Excel, JSON, SQL).
    •    - Web Scraping: Extracting data from websites using libraries like BeautifulSoup or Scrapy.
    •    - API Integration: Fetching data from APIs (e.g., RESTful APIs) using libraries like requests.
    •    - Data Cleaning: Handling missing values, outliers, and inconsistencies in the data.
    •    - Data Transformation: Reshaping data, encoding categorical variables, scaling numeric features.

     2. Exploratory Data Analysis (EDA):

    •    - Descriptive Statistics: Calculating summary statistics (mean, median, variance, etc.).
    •    - Data Visualization: Creating visualizations using libraries like Matplotlib, Seaborn, and Plotly.
    •    - Correlation Analysis: Examining relationships between variables.
    •    - Distribution Analysis: Understanding the distribution of data features.
    •    - Dimensionality Reduction: Applying techniques like PCA (Principal Component Analysis) or t-SNE for visualization and feature selection.

     3. Data Manipulation and Analysis:

    •    - Pandas Fundamentals: Working with Series and DataFrame objects, indexing, slicing, and filtering data.
    •    - Grouping and Aggregation: Performing group-wise operations on data.
    •    - Merging and Joining: Combining multiple datasets based on common keys.
    •    - Time Series Analysis: Handling time-stamped data, resampling, and time series decomposition.

     4. Statistical Analysis:

    •    - Probability Distributions: Understanding common probability distributions (normal, binomial, Poisson, etc.).
    •    - Hypothesis Testing: Conducting hypothesis tests (t-tests, chi-square tests, etc.) for statistical inference.
    •    - ANOVA and Regression Analysis: Performing analysis of variance (ANOVA) and regression analysis to model relationships between variables.
    •    - Non-parametric Tests: Utilizing non-parametric tests for analyzing data that do not meet parametric assumptions.

     5. Machine Learning Basics:

    •    - Introduction to Scikit-Learn: Understanding the Scikit-Learn library for machine learning in Python.
    •    - Supervised Learning: Building and evaluating models for classification and regression tasks.
    •    - Unsupervised Learning: Exploring clustering algorithms like K-means and hierarchical clustering.
    •    - Model Evaluation: Assessing model performance using cross-validation, metrics like accuracy, precision, recall, F1-score, and ROC curves.

     6. Advanced Topics:

    •    - Feature Engineering: Creating new features from existing data to improve model performance.
    •    - Model Selection and Tuning: Selecting the appropriate model and optimizing hyperparameters using techniques like grid search or randomized search.
    •    - Ensemble Methods: Understanding ensemble techniques such as random forests, gradient boosting, and stacking.
    •    - Pipeline Construction: Building end-to-end machine learning pipelines for data preprocessing, feature engineering, and model training.

     

     7. Real-world Projects and Case Studies:

    •    - Application of Data Analytics Techniques: Solving real-world problems by applying data analytics techniques to datasets from various domains.
    •    - Competitive Platforms: Participating in Kaggle competitions or similar platforms to gain hands-on experience and learn from peers.
    •    - Capstone Projects: Undertaking end-to-end projects that showcase proficiency in data acquisition, preprocessing, analysis, modeling, and visualization.

     8. Continuous Learning and Professional Development:

    •    - Staying Updated: Keeping up-to-date with the latest developments in Python libraries, machine learning algorithms, and data analytics best practices.
    •    - Community Engagement: Participating in data science communities, attending meetups, and networking with professionals in the field.
    •    - Advanced Courses and Certifications: Pursuing advanced courses or certifications in data science and machine learning to deepen expertise and credentials.

Requirements

    NA

     

Course Curriculum

image not found
20000.00
  • Level
    Basic to Advanced
  • Lectures
    45 Lectures
  • Duration
    60
  • Language
    English
  • Access
    limited
  • Certificate
    no

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