Online Course – Certified Professional Internship in Applied Data Science with R by Coursera, IBM

Build your data science skills with R and SQL. Improve your ability to turn data into information and insights.

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קורס אונליין - התמחות מקצועית מוסמכת במדע נתונים יישומי עם R של Coursera

Professional Certificate

Beginners

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Performing basic programming tasks in the R language.
  • Data preparation, statistical analysis and predictive modeling.
  • Creating relational databases and querying data using SQL and R.
  • Communicating findings using data visualization techniques.
  • Working with different data sources.
  • Working with data sets.
  • Using SQL and relational databases.
  • Using the R programming language.
  • Working with tools like R Studio, Jupyter Notebooks, and R libraries related to data science.

What you will learn in the course

Courses for which the course is suitable

  • Entry-level data scientist
  • Data Analyst
  • Software developer with specialization in R
  • Data visualization expert
  • Database Administrator
  • Business Analyst
  • Data scientist
  • Predictive Model Developer
  • SQL programmer
  • Statistical analysis specialist

Internship – Series of 5 courses

This internship is designed for anyone who has a passion for learning and is interested in developing skills, tools, and a portfolio that will give them a competitive advantage in the job market as an entry-level data scientist.

During these five online courses, you will develop the skills needed to integrate and leverage different data sources using the R programming language to transform data into insights that help you and your stakeholders make more informed decisions.

What will you learn?

  • Performing basic programming tasks in the R language.
  • Data preparation, statistical analysis and predictive modeling.
  • Creating relational databases and querying data using SQL and R.
  • Communicating findings using data visualization techniques.

Hands-on Learning Project

During the internship, you will complete practical labs that will help you gain practical experience with:

  • Different data sources.
  • Data sets.
  • SQL and relational databases.
  • The R programming language.

You will work with tools like R Studio, Jupyter Notebooks, and R libraries related to data science, including dplyr, Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest.

In the final course of the specialization, you will complete a final project in which you will apply what you have learned to a challenge that requires data collection, analysis, basic hypothetical experimentation, visualization, and modeling required on real-world data sets.

Details of the courses that make up the specialization

Introduction to R Programming for Data Science

Course 1
10 hours
4.5 (450 ratings)

What you’ll learn

  • Work with basic data types in the R language using RStudio or Jupyter Notebooks.
  • Control program flow with conditions and loops, write functions, perform operations on character strings, write regular expressions, handle errors.
  • Build and manage data structures in the R language, including vectors, factors, lists, and data frames.
  • Read, write, and save data files and scrape data from websites using R.

Skills you will develop

  • Data Science
  • Linear regression
  • Data visualization
  • Programming in R language
  • Exploratory data analysis

SQL for Data Science with R

Course 2
27 hours
4.3 (134 ratings)

What you’ll learn

  • Create and access a database on the cloud.
  • Write and execute basic SQL commands – SELECT, INSERT, UPDATE, DELETE, CREATE, DROP.
  • Build SQL commands to filter, sort, group results, use built-in functions, write nested queries, access multiple tables.
  • Analyze data from Jupyter using R and SQL by combining SQL and R skills to query real-world data sets.

Skills you will develop

  • Data Science
  • Programming in R language

Data analysis with R

Course 3
16 hours
4.7 (285 ratings)

What you’ll learn

  • Prepare data for analysis by handling missing values, filtering and regularizing data, grouping, and translating categorical values ​​into numerical values.
  • Compare predictive models that use simple linear regression, multiple linear regression, and polynomial regression methods.
  • To study data using descriptive statistics, data sets, analysis of variance (ANOVA), and correlation statistics.
  • Evaluate a model for overfitting and underfitting situations and calibrate its performance using regularization and grid search.

Skills you will develop

  • Data Science
  • Data Analytics
  • Statistical analysis
  • Data visualization
  • Programming in R language

Data visualization with R

Course 4
12 hours
4.6 (219 ratings)

What you’ll learn

  • Create graphs such as: bar charts, histograms, pie charts, scatter graphs, line graphs, box plots, and maps using R and related packages.
  • Design custom graphs using notes, axis titles, text labels, themes, and breakdowns.
  • Create maps using the Leaflet package for R.
  • Create interactive dashboards using the Shiny package for R.

Skills you will develop

  • Data Science
  • Data Analytics
  • Data visualization
  • Programming in R language

Data Science with R – Final Project

Course 5
24 hours
4.6 (76 ratings)

What you’ll learn

  • Write a program to scrape data from an HTML file using HTTP requests and convert the data into a data frame.
  • Prepare data for models by handling missing values, filtering and regularizing data, grouping, and converting categorical values ​​to numerical values.

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