In no other project course, you will find such detailed mathematics behind the concepts ” – Abhishek

Base R-Programming

Get started with R-Programming with 9+ hours of in-depth content. Great for beginners and for data scientists trying to learn R as an added skill

4.6

4.6/5

(62 ratings)

527 students

“In no other project course, you will find such detailed mathematics behind the concepts” – Abhishek

Base R- Programming

Get started with R-Programming with 9+ hours of in-depth content. Great for beginners and for data scientists trying to learn R as an added skill

4.6

4.6/5

(62 ratings)

527 students

$12.00
  $27.99  40% off

30-Day Money Back Guarantee

What you'll learn

This course includes

What you'll learn

$12.00
  $27.99  40% off

30-Day Money Back Guarantee

Course Curriculum

8 Sections • 74 Lectures  • 9h 49 min total length

  • Course  Overview (1:07) [Preview]
  • Installing R Studio (5:44)

  • Datatypes (5:55)
  • Data Structures (3:18)

  • Vectorization (2:44)
  • Online Pizza Advertisment (2:25)
  • Create vector with single element (10:09)
  • Create group of elements in vector (12:01)
  • Use of repetitions and sequence to create a vector (5:49)
  • Random numbers, sampling and rounding (8:42)
  • Formatting numbers (2:35)
  • Create subsets (7:34)
  • Handling missing values (10:06)
  • Binning (9:13)
  • Operations within a vector (8:55)
  • Operations within same vectors (3:13)
  • Operations between different sized vectors (3:42)
  • Revenue impact of ad-campaign (4:09)

  • Module overview (0:42)
  • Set operations (5:06)
  • if and if-else (3:30)
  • Making assignments within if-else (1:50)
  • Checking existence (1:14)
  • Nested 'ifelse' (3:28)
  • For loops (4:23)
  • Writing smarter 'for loops' (1:45)
  • Break while repeat (5.11)
  • Memory pre-allocation tactics (4:38)
  • Why dates cant just be strings (4:34)
  • Date operations (1:21)
  • Working with lubridate and anytime (4:29)

  • Introduction to lists (5.32)
  • Unnamed list, unlist and more (6:36)

  • Introduction to dataframe (2:47)
  • Creating Dataframe (3:08)
  • Visual editing (1:46)
  • Various dataframe operations (11:00)
  • Inspecting and rownames (10:14)
  • Select and delete subset (6.34)
  • Attributes and comments (4:22)
  • Saving dataframe to disk(6:50)
  • Native RDS files (3:22)
  • Handling CSV files (1:44)
  • xlsx files (1:58)
  • SAS and stata files (3:52)
  • R-datasets, packages and public datasets (11:14)
  • Useful data summarization functions (8:13)
  • Conditional filtering and missing values (9:54)
  • Matrix vs dataframe (6:21)
  • Joining operations for dataframes (9:28)
  • Pivot and frequency table (10:58)
  • Grouping and solving case problem (9:22)

  • Module overview (1:30)
  • Base graphics (2:15)
  • Scatterplot (1:01)
  • Adding plot components (8:06)
  • Legend (1:17)
  • Saving plot components and challenge (2:49)
  • Line plots with secondary Y axis (6:29)
  • Change par settings (4:08)
  • Histogram and bar charts (6:02)
  • Box plot (3:22)
  • Dot plots and density plots (5:12)
  • Multiple plots and custom layouts (5:24)

  • Introduction to stringr (4:47)
  • Sentences,  punctuations, string manipulations (5:34)
  • Writing effective functions (6:46)
  • Local and global namespace (12:16)
  • Debugging R code (9:07)
  • Error handling (7:12)
  • Apply function (8:18)
  • Lapply, sapply and vapply (3:56)
  • Mapply (2:46)

Requirements

About the course

Malware attacks affect not just individual consumers, but also enterprises and governments. And as a provider of operating system software, Microsoft takes this problem very seriously.

In this course you will solve this problem by predicting whether a computer is going to be attacked by malware or not. You’ll learn end-to-end project steps, in-depth concepts, real world tips and tricks, and the full code involved in building the actual data science solution.

You will learn the following skills by the end of the course:

LightGBM XGBoost Random Forest Decision Tree Logistic Regression Hyperparameter Tuning Feature Importance Confusion Matrix ROC AUC Concordance and Discordance Precision Recall Curve Capture Rates and Gains Feature Engineering Label Encoding Frequency Encoding Chi-Square test ANOVA test Exploratory Data Analysis Memory Optimization Data Preprocessing

Who is this course for

Instructor

Selva Prabhakaran

Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this course!

I head the Data Science team for a global Fortune 500 company, and over the last 10 years of my data science experience I’ve deployed 20+ global products. I’m also the Founder & Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student request, I’ve put up the series of projects and courses with detailed explanations. Hope you love this course!

Student Reviews

Lorenna Christina

Lorenna Chrisitina

The instructor has a wide range of knowledge What I liked most about the classes is how the instructor explained the mathematics behind the algorithms easily, which in turn makes the course more interesting. I definitely recommend this project!

Moinak Dey

Moinak Dey

Data Science could not have been explained easier than this. All the major algorithms were explained beautifully from scratch and the practical tips are noteworthy as well.

Jyoti Goyal

Jyoti Goyal

Loved the way the course guided me through the entire project solving journey. Helped build my confidence for end-to-end implementation

Souptik Dhar

Jyoti Goyal

I was able to get a first hand feel of solving a project with large amounts of data and multiple modeling techniques, just like a work-experience

FAQs

This is a completely self-paced online course – you decide when you start and when you finish. On an average, students have finished this project course in 2 weeks.

If you are a data science aspirant preparing to break into role of data scientist or if you are a data scientist trying to get additional programming skills, this course is for you. Check our pre-requisites section for more details.

Yes, all data, codes and notebooks are shared as downloadable resources within the course

You will have access to the video course access for 1 year. You can retain the downloadable content (i.e. complete dataset,  notebooks and codes) forever.

Yes, you will get certification of course completion, which will also mention the key learning of the course

Every lecture has a comment section. You can write your question in the comments, and the instructor and the team will get back to you.  You can also check other students’ comments to check if it has already been asked and answered.

We  are confident about our content and are sure that you’ll find value in it. However, in case you are unhappy with the course, you can drop a mail to [email protected] in the first 30 days, and we will give you a full refund.

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