Introduction to Data Science

About Course

Course Description:

This Introduction to Data Science course is designed to provide students with a foundational understanding of the key concepts and techniques in data science. Through a combination of lectures, hands-on exercises, and real-world projects, students will learn how to collect, analyze, and interpret data to make data-driven decisions.

Assessment and Grading:

Weekly assignments and quizzes (40%)
Mid-term data analysis project (20%)
Final data science project (30%)
Class participation and attendance (10%)

By the end of this course, students should be able to:
Understand the data science process and its applications.
Collect, clean, and preprocess data for analysis.
Perform exploratory data analysis and visualization.
Apply machine learning algorithms to solve real-world problems.
Create informative and visually appealing data visualizations.
Communicate data-driven insights effectively.
Work on a data science project from start to finish

Course Content

Week 1-2: Introduction to Data Science
Understanding the role of data science in various industries Overview of the data science process Introduction to data collection and cleaning Common tools and libraries (e.g., Python, pandas)

Week 3-4: Exploratory Data Analysis (EDA)
Descriptive statistics and data summary Data visualization (matplotlib, seaborn) Data preprocessing and cleaning Identifying patterns and outliers in data

Week 5-6: Data Manipulation and Analysis
Data wrangling with pandas Aggregation and grouping Feature engineering and transformation Hypothesis testing and statistical analysis

Week 7-8: Machine Learning Fundamentals
Introduction to machine learning Supervised learning vs. unsupervised learning Building and evaluating machine learning models Cross-validation and overfitting

Week 9-10: Machine Learning Algorithms
Linear regression and logistic regression Decision trees and random forests Clustering algorithms (k-means, hierarchical clustering) Dimensionality reduction techniques (PCA)

Week 11-12: Data Visualization and Communication
Advanced data visualization with libraries like Plotly or Bokeh Storytelling with data Creating interactive dashboards Final data science project presentations

Additional Topics (Optional):
Natural language processing (NLP) Time series analysis Deep learning and neural networks Big data tools and techniques (e.g., Apache Spark)

Student Ratings & Reviews

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About Course

Course Description:

Assessment and Grading:

What Will You Learn?

Course Content

Week 1-2: Introduction to Data Science
Understanding the role of data science in various industries Overview of the data science process Introduction to data collection and cleaning Common tools and libraries (e.g., Python, pandas)

Week 3-4: Exploratory Data Analysis (EDA)
Descriptive statistics and data summary Data visualization (matplotlib, seaborn) Data preprocessing and cleaning Identifying patterns and outliers in data

Week 5-6: Data Manipulation and Analysis
Data wrangling with pandas Aggregation and grouping Feature engineering and transformation Hypothesis testing and statistical analysis

Week 7-8: Machine Learning Fundamentals
Introduction to machine learning Supervised learning vs. unsupervised learning Building and evaluating machine learning models Cross-validation and overfitting

Week 9-10: Machine Learning Algorithms
Linear regression and logistic regression Decision trees and random forests Clustering algorithms (k-means, hierarchical clustering) Dimensionality reduction techniques (PCA)

Week 11-12: Data Visualization and Communication
Advanced data visualization with libraries like Plotly or Bokeh Storytelling with data Creating interactive dashboards Final data science project presentations

Additional Topics (Optional):
Natural language processing (NLP) Time series analysis Deep learning and neural networks Big data tools and techniques (e.g., Apache Spark)

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UK

Nigeria

About Course

Course Description:

Assessment and Grading:

What Will You Learn?

Course Content

Week 1-2: Introduction to Data Science Understanding the role of data science in various industries Overview of the data science process Introduction to data collection and cleaning Common tools and libraries (e.g., Python, pandas)

Week 3-4: Exploratory Data Analysis (EDA) Descriptive statistics and data summary Data visualization (matplotlib, seaborn) Data preprocessing and cleaning Identifying patterns and outliers in data

Week 5-6: Data Manipulation and Analysis Data wrangling with pandas Aggregation and grouping Feature engineering and transformation Hypothesis testing and statistical analysis

Week 7-8: Machine Learning Fundamentals Introduction to machine learning Supervised learning vs. unsupervised learning Building and evaluating machine learning models Cross-validation and overfitting

Week 9-10: Machine Learning Algorithms Linear regression and logistic regression Decision trees and random forests Clustering algorithms (k-means, hierarchical clustering) Dimensionality reduction techniques (PCA)

Week 11-12: Data Visualization and Communication Advanced data visualization with libraries like Plotly or Bokeh Storytelling with data Creating interactive dashboards Final data science project presentations

Additional Topics (Optional): Natural language processing (NLP) Time series analysis Deep learning and neural networks Big data tools and techniques (e.g., Apache Spark)

Student Ratings & Reviews

About Us

Quick Links

Recent Trainings

Get intouch

Contact Us

UK

Nigeria

Week 1-2: Introduction to Data Science
Understanding the role of data science in various industries Overview of the data science process Introduction to data collection and cleaning Common tools and libraries (e.g., Python, pandas)

Week 3-4: Exploratory Data Analysis (EDA)
Descriptive statistics and data summary Data visualization (matplotlib, seaborn) Data preprocessing and cleaning Identifying patterns and outliers in data

Week 5-6: Data Manipulation and Analysis
Data wrangling with pandas Aggregation and grouping Feature engineering and transformation Hypothesis testing and statistical analysis

Week 7-8: Machine Learning Fundamentals
Introduction to machine learning Supervised learning vs. unsupervised learning Building and evaluating machine learning models Cross-validation and overfitting

Week 9-10: Machine Learning Algorithms
Linear regression and logistic regression Decision trees and random forests Clustering algorithms (k-means, hierarchical clustering) Dimensionality reduction techniques (PCA)

Week 11-12: Data Visualization and Communication
Advanced data visualization with libraries like Plotly or Bokeh Storytelling with data Creating interactive dashboards Final data science project presentations

Additional Topics (Optional):
Natural language processing (NLP) Time series analysis Deep learning and neural networks Big data tools and techniques (e.g., Apache Spark)