Data science using python

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Data Science / Analytics

Data Science using Python

Couse Completed

105 Data Science

Upcomming Batch

15 Sept, 2024

Review

About Course

The Data Science using Python course provides a comprehensive introduction to data science concepts and techniques using Python programming. Students will learn how to collect, clean, and analyze data, as well as how to use various Python libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn. The course covers essential data science methods, including statistical analysis, machine learning algorithms, and data visualization. Participants will work on practical projects to apply their knowledge and solve real-world data problems, ultimately gaining the skills needed to extract insights and make data-driven decisions.

Course Objectives

Understand fundamental data science concepts including data collection, cleaning, and analysis using Python.
Gain proficiency in using Python libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn for data manipulation and visualization.
Learn to perform statistical analysis and interpret data to extract meaningful insights.
Develop skills in applying machine learning algorithms to solve data problems and make predictions.
Acquire experience in building and evaluating data models to inform data-driven decision-making.
Work on practical projects to apply data science techniques to real-world datasets and challenges.

Course Curriculum

Overview of Data Science

Introduction to Python Programming

Setting Up the Python Environment (Anaconda, Jupyter Notebooks)

Data Collection Techniques

Importing and Exporting Data (CSV, Excel, SQL)

Data Cleaning and Preprocessing

Handling Missing Values and Outliers

Data Visualization Techniques

Using Matplotlib and Seaborn for Plotting

Summary Statistics and Data Aggregation

Correlation and Distribution Analysis

Data Structures: Series and DataFrames

DataFrame Operations: Selection, Filtering, and Aggregation

Merging, Joining, and Concatenating Datasets

GroupBy and Pivot Tables

Descriptive Statistics

Probability Distributions

Hypothesis Testing and Confidence Intervals

ANOVA and Regression Analysis

Supervised vs. Unsupervised Learning

Overview of Common Algorithms (Linear Regression, Decision Trees, K-Means Clustering)

Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)

Cross-Validation and Hyperparameter Tuning

Data Preprocessing for Machine Learning

Implementing and Training Models

Evaluating Model Performance

Feature Selection and Engineering

Interactive Visualizations with Plotly

Creating Dashboards with Dash

Advanced Plotting Techniques

Ratings & Reviews

4.5

Rated 4 out of 1 Rating

5 star

82%

4 star

30%

3 star

15%

2 star

6%

1 star

10%

Featured review

Devi

2 weeks ago

I learned so much from this course. The real-world examples and hands-on projects made the concepts easy to understand. I'm now able to create detailed reports and dashboards with confidence.

Helpful?

Veni

2 weeks ago

he course was challenging, but in a good way. The assignments pushed me to think critically and apply what I learned. By the end, I felt confident in my ability to build and evaluate ML models.

Helpful?

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