Data science is a major asset in the modern world and is used in many industries and fields. This field assists in finding patterns in vast sets of data to enhance the work of students, professionals, researchers, and policymakers. Glassdoor recognizes data science as the most popular job, and the average salary in this field is over $120000, so the occupation is very promising and paid.
Our boot camp, "Data Science with R: A Comprehensive Bootcamp,” equips the learners with the knowledge and skills needed in data science when using the R programming language. It is designed specifically so that you will leave the classroom and apply the acquired knowledge in practical learning sessions that form a solid basis regarding data science concepts and related tools and methods. You will learn from importing data from different sources, such as CSV and Excel, to data visualization using some of the packages in R, such as ggplot. , missing values handling, data manipulation and other descriptive aspects will also be included.
Other subjects that you will study include hypothesis testing, the different parametric and non-parametric tests, and the types of learning, such as supervised and unsupervised learning algorithms. The following techniques will be covered: linear and logistic classification, decision trees, Neural Networks and clustering. We will introduce you to accuracy, precision, recall, and F1-score to perform a correct evaluation of the models. By the end of the boot camp, "Data Science with R: Focused training of this sort under the title “Data Science with R: A Comprehensive Bootcamp” will fully prepare you to solve real-life data science problems.
Data Science with R: A Comprehensive Bootcamp Table of Contents:
- Course Overview: 06:01
- Course Structure: 03:53
- Introduction to Data Science, Concepts, and its Significance: 26:48
- Intro to R and R-Studio: 49:30
- Functions and Control Structures: 29:18
- Data Importing and Piping: 01:07:21
- EDA Intro 1: Missing Data and Data Transformation: 23:54
- EDA Intro 2: Data Types and Data Distribution Concept: 27:33
- Missing Data and Data Imputation: 41:03
- Data Visualization: 53:23
- Normal Distribution and Outlier Handling: 31:43
- Intro to Statistical Analysis: 33:32
- Parametric Tests 1: 53:36
- Parametric Tests 2: 32:02
- Non-Parametric Tests: 01:02:28
- Intro to Machine Learning: Supervised and Unsupervised: 29:30
- Simple and Multiple Linear Regression: 20:04
- Logistic Regression: 19:49
- Decision Tree: 05:50
- Support Vector Machine: 12:33
- Artificial Neural Network: 23:35
- Clustering: PCA, K-means Clustering, and Hierarchical Clustering: 34:45
Who is this course for?
- Students, Working people, Scholars, and learners.
- Entrepreneurs and Start-ups
- Therefore, the major audience in the current study will consist of government officials and policymakers.
- Non-profit Organizations
- Would Be Wise To Really Pay Attention To It If They Really Want to Understand It
