10 Best DataCamp Courses & Tutorials [2019]

Best DataCamp course tutorial class certification training online

25 Experts have compiled this list of Best DataCamp Course, Tutorial, Training, Class, and Certification available online for 2019. It includes both paid and free resources to help you learn DataCamp. These courses are suitable for beginners, intermediate learners as well as experts.

 

10 Best DataCamp Courses, Tutorials, Certification Online [2019]

1. DataCamp’s Data Scientist with Python Tutorial

It is a fact that Python is one of the key languages that data scientists swear by. In this course, you will focus on the key concepts such as syntax, error handling, and iterators to name a few. Following this, master the techniques required to analyze large sets of data and draw meaningful insights from them. Perform scientific computations with numpy, write your own functions and visualize the insights gained using Pandas. By the end of this career track, you will be proficient in the statistical and ML topics that will allow you to analyze complex data using Python programming.

 

Key USPs –

– Understand how to import all types of data from different sources like internet, Excel sheet, database, and SAS and cleanse them.

– Work with industry level libraries and tools that will give you a feel of the real world challenges faced in this field.

– Integrate and implement your knowledge on the project where you will get to explore the Stanford Open Policing dataset and analyze how gender impacts police behavior.

– Master the fundamentals of databases and how to use them efficiently for analysis.

– This career track includes a total of 26 courses with hands-on lessons.

 

Duration: 84 hours

Rating: 4.5 out of 5

You can Sign up Here

 

 

2. Python Programming Tutorial with DataCamp

This track is designed to help you pick up the skills of working with one of the most in-demand programming languages today. Throughout these four programs, primarily aimed at beginners you will explore the basic syntaxes, flow structures and important data structures such as lists, tuples, and dictionaries. Not only this, once you have built a strong footing you can move on to the intermediate level topics and use matplotlib and Pandas for presenting your findings from the data using visualizations to communicate to different audiences.

 

Key USPs-

– Learn to debug error and understand the scope of variables and other entities while writing the programs.

– Easy interactive lectures that make the learning a fun experience.

– Complete all the lectures and assignments to earn the statement of accomplishment.

– Plenty of examples that demonstrate the concepts every step of the way.

 

Duration: 15 hours

Rating: 4.5 out of 5

You can Sign up Here 

 

 

3. Data Scientist with R  on DataCamp

R is one of the programming languages that play a crucial role in data science, mostly for statistical analysis. So in this track, you will master the fundamentals techniques required for analyzing data by manipulating data structures like vectors, matrices and data frames. Explore loops, conditional statements, vector functions before getting introduced to the tools that you will require to put the theory into action.

 

Key USPs-

– Import and parse data from various sources like CSV, XLS and text files with tools such as readxl and data.table.

– Prepare, cleanse and structure the datasets before drawing information from them as per requirement.

– All the topics are covered from scratch, so no prior experience is required in these lectures.

– Combine databases to gain results that are not possible from working on individual tables.

– Bring Unix command line into the equation to automate repetitive tasks and run codes on the cloud.

 

Duration: 94 hours

Rating: 4.5 out of 5

You can Sign up Here

 

 

4. R Programmer Tutorial

If you are interested in getting introduced to R programming language then these series of programs are worth checking out. Commence by learning about the basic datatypes, assigning variables creating vectors and working with matrices and other crucial topics. You will also get the chance to explore the collection of relevant tools and software such as ggplot2, and dplyr. With the hands-on classes, you will gain the confidence to wrangle data and develop analytics tools.

 

Key USPs-

– Write optimal codes by applying parallel programming, benchmarking and profiling.

– Reduce program complexity using object-oriented techniques with S3, R6 systems.

– Earn the statement of accomplishment by completing all the 10 courses.

– Numerous real-world examples are thoroughly explained in relation to the relevant topic.

 

Duration: 43 hours

Rating: 4.5 out of 5

You can Sign up Here 

 

 

5. Machine Learning with Python Learning Path

If you are familiar with Python and would like to build upon your skill set so that you can jump-start a career in machine learning then this career track is here to guide throughout the journey. The introductory classes talk about classification problems and finding a solution for them using supervised learning techniques. Tools such as scikit-learn and scipy are useful in extracting information irrespective of the state of the data. End the classes by working on a project that will let you build a model as per the problem statement.

 

Key USPs –

– Discover pattern in data and sort them into clusters.

– Understand the basics of unsupervised learning and explore the relevant algorithms.

– Train, test and tune the linear classifiers using Python.

– Join the community chat to share ideas and clarify doubts.

– The flexible deadlines allow you to learn as per your schedule.

 

Duration: 20 hours

Rating: 4.5 out of 5

You can Sign up Here

 

 

6. Statistics Fundamentals with R

Statistical analysis is an important part of understanding of what any dataset actually represents and this track uses R to cover these topics as it is one of the top choices for these type of tasks. In the initial parts of the classes, you will learn to identify a problem and collect data relevant to it. Following this, you can start summarising real-world datasets by getting hands-on and working on a case study and using logical regression for classification. End the journey by learning about basic experimental designs and statistical tests.

 

Key USPs-

– The introductory lectures are available for free which allows you to get an idea if this is the right course for you.

– Add the statement of accomplishment to your portfolio post completion of the track.

– Learn the methods to fit, visualize and interpret the solution models.

-The relaxed and interactive teaching style of the instructor creates a great learning environment.

– Case studies are useful in providing an idea about real-world scenarios.

 

Duration: 20 hours

Rating: 4.5 out of 5

You can Sign up Here 

 

 

7. Text Mining with R Course

Text is undoubtedly one of the most inexpensive forms of data and in many scenarios are replacing other unstructured types of sources. To understand everything that you can do with text, this course will show you how to make use of the information, as well as cleanse, summarize and model it. Navigate through the tools that are used to perform sentiment analysis, run and interpret topic models as well as visualize text.

 

Key USPs –

– Extract text from different sources and find patterns in them using regular expressions and stringr.

– Understand the theory and then apply your knowledge in a real-life based case study.

– Get complete guidance to get acquainted with various tools require throughout the track.

– Attempt plenty of available practical assignments and showcase your skills in your resume.

– Some of the introductory lessons are available for free.

 

Duration: 16 hours

Rating:

You can Sign up Here

 

8. Data Analyst with Python

In this career track, you will gain the skills required to jump-start a career as a data analyst. In the initial classes, you will learn to harness the power of Python in this field by becoming familiar with the basic syntax and popular modules such as Matplotlib and Pandas. In addition to this, you will also explore the techniques to extract information from databases and visualize your insights and communicating them with different crowds.

 

Key USPs-

– Explore databases such as MySQL, SQL Server, Oracle, and PostgreSQL.

– Improve your import skills by handling web and API data.

– Work with real-world datasets including string and numeric data.

– Deal with missing values and outlier by diagnosing data for problems.

– Get acquainted with the jargons required in this field.

 

Duration: 60 hours

Rating: 4.5 out of 5

You can Sign up Here 

 

 

9. Learn R

If you are interested in learning about R and are not sure about where to start then you can take your pick from the array of options available. You can decide to take all the courses so that to get complete knowledge or just take one particular program to work on one particular skill. Some of the top choices include an introduction to R, cleaning data, visualization and more. If you are looking simply for practice then there is an option for that as well.

 

Key USPs-

– Improve your knowledge by working on tons of fun exercises and discovering your weak areas.

– Navigate through various tools and produce meaningful visualizations.

– Interact with your peers and instructors to overcome your doubts and build a solid foundation.

– The beginner level of lessons are available for free and do not need any prerequisite.

 

Duration: Self-paced

Rating: 4.5 out of 5

You can Sign up Here 

 

 

10. Data Visualization with R

Extracting information from data is indeed an important task but it is equally important to present the data efficiently so that the knowledge gained from it can be used properly to make further strategies and decisions. If you want to learn by doing then this track will not disappoint you. After covering the fundamentals you can get started in working with ggplot2. Understand the grammar of the graphics and explore the themes, coordinates and more that the tool has to offer. Apart from this work with multivariate datasets, and base graphics.

 

Key USPs-

– Explore various charts, visualization types among others to convey your data.

– Gain best practices and advice from the instructors and use them in your visualization.

– Tons of available exercises to brush up the concepts covered and measure your grasp on the topics.

– Get introduced to the lattice package and implement Trellis graphics in R.

 

Duration: 24 hours

Rating: 4.5 out of 5

You can Sign up Here 

 

So these were the 10 Best DataCamp Tutorial, Class, Course, Training & Certification available online for 2019. Hope you found what you were looking for. Wish you a Happy Learning!