New Year '20 Offer ~~ ~~

# Data Scientist Training (76 Courses, 60+ Projects)

76 Online Courses

60 Hands-on Projects

632+ Hours

Verifiable Certificate of Completion

Lifetime Access

Python Data Scientist Courses (18 Courses)

Python Data Scientist Projects (6 Projects)

R Programming Data Scientist Courses (12 Courses)

R Programming Data Scientist Projects (11 Projects)

SAS Data Scientist Courses (9 Courses)

#### What you get

###### Online Classes

###### Technical Support

###### Mobile App Access

###### Case Studies

## Online Data Scientist Course

This Data Scientist Training Course includes **76comprehensive data science courses, 60 Projects with 632+ hours of video tutorials and Lifetime Access**. You will also get verifiable certificates (unique certification number and your unique URL) when you complete each of the 76 courses, 60 Projects.

Data Science is a field that uses various algorithms and processes to extract insights and hidden information from datasets which can’t be seen, it is looking at the dataset.

Data science is a mutual blend of Programming, Math’s or statistics and not the least is domain knowledge. This data scientist course will offer you a high-quality e-learning course, that will ultimately take you slowly to the optimal path of a data scientist. However, this data scientist course will be benefited to the data analyst, data visualization experts, data engineers, analytics project managers, etc.

As the era of big data has arrived, the need is not only to collate data but also to massage datasets as per the business requirements. The core of data science is data. The greater is the quality of data, the greater is the quality of insights and hence help to make crucial decisions about further strategies and goals for the organization.

#### Industry Growth Trend

[Source - MarketsandMarkets]

#### Average Salary

[Source - Indeed]

## About Data Scientist Course

Courses | No. of Hours | |
---|---|---|

Machine Learning with SciKit-Learn in Python | 8h 46m | |

Projects on ML - Predictive Modeling with Python | 9h 44m | |

Matplotlib for Python Developers - Beginners | 4h 24m | |

Matplotlib for Python Developers - Intermediate | 2h 6m | |

Matplotlib for Python Developers - Advanced | 6h 52m | |

Pandas with Python Tutorial | 6h 05m | |

NumPy and Pandas | 5h 07m | |

Project on Pandas - Data Management for Retail Dataset | 4h 18m | |

Project on Python - Sentiment Analysis | 1h 16m | |

Data Science with Python | 4h 22m | |

Artificial Intelligence with Python | 6h 36m | |

Video Analytics Using Opencv and Python Shells | 2h 21m | |

Machine Learning using Python | 3h 39m | |

Statistics for Data Science using Python | 3h 56m | |

Project on Tensorflow - Implementing Linear Model with Python | 1h 49m | |

Project - Data Analytics with Data Exploration Case Study | 5h 19m | |

Project on ML - Random Forest Algorithm | 1h 32m | |

Seaborn - Beginners | 2h 35m | |

Seaborn - Intermediate | 1h 22m | |

Seaborn - Advanced | 1h 58m | |

PySpark - Beginners | 2h 33m | |

PySpark - Intermediate | 2h 16m | |

PySpark - Advanced | 1h 21m | |

Machine Learning with Python Project - Predict Diabetes on Diagnostic Measures | 1h 07m | |

R Studio UI and R Script Basics | 4h 11m | |

R Programming for Data Science | A Complete Courses to Learn | 6h 22m | |

Project - Logistic Regression with R | 4h 25m | |

Project - Decision Tree Modeling using R | 1h 45m | |

Project on ML - Churn Prediction Model using R Studio | 1h 26m | |

Financial Analytics in R - Beginners | 3h 53m | |

Financial Analytics in R - Intermediate | 1h 32m | |

Financial Analytics in R - Advanced | 1h 43m | |

R for Finance - Beginners to Beyond | 2h 18m | |

Comprehensive Course on R | 4h 1m | |

Project on R - Forecasting using R | 4h 47m | |

Project - Fraud Analytics using R & Microsoft Excel | 2h 37m | |

Project - Marketing Analytics using R and Microsoft Excel | 3h 32m | |

Machine Learning with R | 20h 21m | |

Projects on R and Tableau - Customer Analytics | 2h 11m | |

Projects on R and Tableau - Pricing Analytics | 2h 02m | |

Business Analytics using R - Hands-on! | 16h 11m | |

Project - Market Basket Analysis in R | 39m | |

Project - Hypothesis Testing using R | 3h 13m | |

Data Visualization with R Shiny - The Fundamentals | 44m | |

Data Science with R | 5h 8m | |

R Studio Anova Techniques Course | 2h 18m | |

SAS Business Analytics for Beginners | 10h 59m | |

Project on SAS - Predictive Modeling with SAS Enterprise Miner | 9h 35m | |

Project on SAS - Quantitative Finance | 3h 34m | |

SAS Statistics | 8h 36m | |

SAS Output Delivery System(ODS) | 9h 08m | |

SAS PROC SQL | 14h 14m | |

SAS Macros Tutorials | 7h 38m | |

Project on SAS - Advanced Analytics | 12h 34m | |

Project on SAS - SAS Graph | 2h 12m | |

Project on SAS - Programming using SAS DS2 | 5h 2m | |

SAS Advanced Project - SAS SQL | 3h 03m | |

SAS Advanced Project - Macros | 5h 12m | |

SAS Advanced Programming | 11h 24m | |

Project on SAS - Categorical Data Analysis | 7h 26m | |

Certified SAS Base Programmer | 12h 48m | |

SAS Features for Starters | 1h 54m | |

SAS PROC SQL Features | 1h 53m | |

Big Data and Hadoop Training | Online Hadoop Course | 2h 5m | |

Hadoop Architecture and HDFS | 6h 24m | |

MapReduce - Beginners | 3h 31m | |

MapReduce - Advanced | 5h | |

Hive - Beginners | 2h 55m | |

Hive - Advanced | 5h 2m | |

PIG - Beginners | 2h 15m | |

PIG - Advanced | 2h 18m | |

NoSQL Fundamentals | 2h 04m | |

Mahout | 3h 55m | |

Apache Oozie | 2h 18m | |

Apache Flume | 20m | |

Apache Storm | 2h 14m | |

Apache Avro | 26m | |

Apache Spark - Beginners | 1h 52m | |

Apache Spark - Advanced | 5h 27m | |

Splunk Fundamentals | 8h 44m | |

Splunk Advanced 01 - Knowledge Objects | 9h 46m | |

Splunk Advanced 02 - Administration | 37h 54m | |

Project on Hadoop - Sales Data Analysis | 47m | |

Project on Hadoop - Tourism Survey Analysis | 55m | |

Project on Hadoop - Faculty Data Management | 35m | |

Project on Hadoop - E-Commerce Sales Analysis | 36m | |

Project on Hadoop - Salary Analysis | 49m | |

Project on Hadoop - Health Survey Analysis using HDFS | 57m | |

Project on Hadoop - Traffic Violation Analysis | 1h 28m | |

Project on Hadoop - Analyze Loan Dataset using PIG/MapReduce | 2h 38m | |

Project on Hadoop - Case Study on Telecom Industry using HIVE | 2h 4m | |

Project on Hadoop - Customers Complaints Analysis using HIVE/MapReduce | 53m | |

Project on Hadoop - Social Media Analysis using HIVE/PIG/MapReduce/Sqoop | 3h 37m | |

Project on Hadoop - Sensor Data Analysis using HIVE/PIG | 4h 31m | |

Project on Hadoop - Youtube Data Analysis using PIG/MapReduce | 3h 07m | |

Hadoop and HDFS Fundamentals on Cloudera | 1h 26m | |

Project on Hadoop - Log Data Analysis | 1h 33m | |

SPSS GUI and Applications | 1h 16m | |

Project on SPSS - Correlation Techniques | 2h 25m | |

Project on SPSS - Linear Regression Modeling | 3h 16m | |

Project on SPSS - Multiple Regression Modeling | 2h 35m | |

Project on SPSS - Logistic Regression | 2h 04m | |

Project on SPSS - Multinomial Regression | 2h 2m | |

SPSS GUI for Statistical Analysis | 2h 08m | |

Tableau | 5h 19m | |

Projects on BI Tools and Tableau Analytics | 5h 48m | |

Business Intelligence with Tableau | 5h 47m | |

Tableau Features Hands-on! | 5h 47m | |

Project on Tableau - Super Store Business Requirements | 46m | |

Analytics using Tableau | 9h 03m | |

Statistical Analysis using Minitab - Beginners to Beyond | 4h 29m | |

Minitab GUI and Descriptive Statistics | 2h 3m | |

ANOVA in Minitab | 53m | |

Correlation Techniques in Minitab | 2h 21m | |

Project on Minitab - Regression Modeling | 9h 54m | |

Minitab Predictive Modeling using Excel | 56m | |

MATLAB - Beginners | 2h 7m | |

MATLAB - Intermediate | 47m | |

MATLAB - Advanced | 4h 9m | |

Oracle SQL | 18h 6m | |

Oracle PLSQL | 13h 21m | |

Oracle DATABASE Admin DBA 1 Course | 9h 45m | |

Machine Learning with Tensorflow | 13h 18m | |

Hands-on Deep Learning Training | 11h 34m | |

Project - Exploratory Data Analysis EDA using ggplot2, R and Linear Regression | 2h 08m | |

Project on R - HR Attrition and Analytics | 2h 52m | |

Project - Logistic Regression using SAS Stat | 4h 33m | |

Project - Linear Regression in Python | 2h 15m | |

Project on Python Data Science - Predicting the Survival of Passenger in Titanic | 2h 11m | |

Project on R - Card Purchase Prediction | 2h 31m | |

Project on Machine Learning - Develop Movie Recommendation Engine with Python | 53m | |

Employee Attrition Prediction using Random Forest Technique and R | 2h 2m | |

Project on Term Deposit Prediction using Logistic Regression CART Algorithm | 1h 43m | |

Poisson Regression Project using SAS Stat | 1h 17m | |

Machine Learning Project using Caret in R | 54m | |

Forecasting the Sales using Time Series Analysis in Python | 2h 26m |

Course Name | Online Data Scientist Course Bundle |

Deal | You get access to all 76 courses, 60 Projects bundle. You do not need to purchase each course separately |

Hours | 632+ Video Hours |

Core Coverage | This Data Scientist course will help you learn data science using R, Python, Machine Learning, Artificial Intelligence, Big data & Hadoop, Predictive Modeling, Business Analytics, Data Visualization and others. |

Course Validity | Lifetime Access |

Eligibility | Anyone who is serious about learning data science and wants to make a career in analytics |

Pre-Requisites | Basic knowledge of data and analytics |

What do you get? | Certificate of Completion for each of the 76 courses, 60 Projects |

Certification Type | Course Completion Certificates |

Verifiable Certificates? | Yes, you get verifiable certificates for each course with a unique link. These link can be included in your Resume/Linkedin profile to showcase your enhanced skills |

Type of Training | Video Course – Self Paced Learning |

System Requirement | 1 GB RAM or higher |

Other Requirement | Speaker / Headphone |

## Online Data Scientist Course Curriculum

To make things easy for you, here’s a comprehensive Data scientist course. You need to invest significant hours to complete this all-in-all course, however, you going to reap its benefit later. Before we ever talk about data science the target audience, FAQs, let’s look at the data scientist training curriculum in detail –

## Goals

The eventual goal of this course is to make one cognizant about Data science and all the approaches that are required to become a data science expert. leveraging unstructured data to extract some useful information will be the primary focus of all the modules and sub-module covered in this course.

## Objectives

The soul objective of this course is very precise and clear. As the course is focused on Data science, our final objective will be to become ample proficient in this technology so that we can become able to work with data science in order to solve real organizational problems. In order to achieve this objective, we will be working on the secondary objective that is to master all the concepts that fall under the domain of data science.

## Course Highlights

This course will help us master programming languages like Python and R. There are various modules based on Python training is available in this course where the trainees will be diving deep into these programming languages with the help of live examples and sample questions. SAS Data Scientist has also explained in the course keeping the hands-on practice in mind. In all the modules that refer to either any tool or technology that endorses data science, things have been detailed using precise examples. The course also includes Hadoop concepts where we will be learning about Hadoop from beginners’ prospective upto the advance one. The questions included in the Hadoop module will be explained by the educator in details and in almost all of the scenario the tainer will be using the real use cases to explain the topics.

Tableau will also be covered in this course. The trainees will learn how to leverage the presentation of data in the graphical form so that one can be able to extract the useful information out of the unstructured data. All the concepts regarding Minitab will also be covered in this course. To explain the working of Minitab, we have included some simple exercises that will help the trainees to understand how things work when we talk about working with Minitab. We will also be learning about Splunk in this training. You will get to learn how to leverage Splunk in order to work with data science. At last, the course will be explaining about Matlab. In the last module, we will be learning Matlab and will master all the features it offers.

## Project Highlights

The course is comprised of modules and projects. In the module, we will cover the concepts that are used to implement data science while in the projects section, we will be working on real projects to make get practical exposure of how things actually work in order to provide a solution for any problem. In this course, we have included projects after every module so that the trainees can get a deep idea about all the concepts immediately after learning them. The course consists of projects on Python programming language where you will learn the advanced aspect of python or about the frameworks that are used to implement the features to leverage data science. For R programming languages, we do have separate projects. In the project based on R, you will get an in-depth idea of how to leverage these programming languages to draft solutions for the organizational level problems.

SAS Data Scientist Projects is one of the main project modules that is added in the course. Under this project, there are six sub-projects and it will take around forty-five hours to complete all these projects. Hadoop based projects have also included in the course where we will be working on six projects topics. It will help the trainees to become ample proficient in working with all aspects of big data. In addition to all of these, one will also be working on projects based on topics like Tableau, Minitab, Matlab, Splunk and so on. After completing this course, the trainee will be able to implement all the features of Data science in production and will help the organization to get some useful information from the heap of raw data.

## Certificate of Completion

## What is Data Science?

Data science is a field where information comes from various sources, which in turn gets converted into valuable insights for business and IT strategies. The data collected from various sources can be structured, unstructured or semi-structured. Massaging the data, formatting the data, analyzing the data and extracting stories out of those data by dashboarding is a generic pipeline structure followed in the data science field.

This Data scientist course will take you through all these various tasks to well-versed you with every part of data science. While practicing Data science, you will come to know about various tools, algorithms and Machine learning principles that are usually used.

Usually people mistaken data science with business intelligence. However, both are completely different. In other words, a higher level of business intelligence can be called data science.

Business intelligence usually deals with structured data, plotting its statistics and visualization. However, data science deals with unstructured, structured and semi-structured data. Data science is not just about statistics and visualization, it also includes predictions and various machine learning concepts.

**Which tangible skills you will learn in this data scientist course? **

Today top-notch companies are relying on Data science skills be it through Python or through some tools like SPSS etc. because of the exponential growth of data in the last decade. And hence they are in dire need of good Data science skills that can support data analysis and build highly reliable and efficient algorithms suitable for specific Organization needs. Below mentioned are some great capabilities and skills, that you will learn through this data scientist course.

**Handling Dirty Datasets:** Through this data scientist training course, you will learn various ways of handling dirty datasets (unstructured data). With a rise in the complexity of data and an increase in business demands, it becomes necessary to handle a wide variety of data like audio, video, etc. It’s not possible that one receives every time a structured data. Knowing data science skills to be it through R, Python, Minitab, SPSS, etc. will help you massage and reform varieties of data.

**Programming skills: **After taking this data scientist training, one will be able to explore, analyze datasets through programming languages like R, Python. Programming skills of these languages will help you target many custom requirements.

**Story Telling: **This data scientist training course will help you get the skills of storytelling. Data science skills finally end up when the insights extracted are well communicated until end users. Hence this data scientist course will tell you how to make a bigger impact on end-user along with great visualization.

**Solve the business problem: **This data scientist course will help you think and code in a way you can solve any business problem efficiently. Manipulating datasets, applying machine learning techniques can be ways of solving business problems.

## Pre-requisites

**Basics of Linear Algebra:**Data Science deals with vectors, matrices, and its operations. If you don’t have a clear picture, it would be great if you can refresh it and then take this data scientist training. It will help you once you go deeper into data science-related algorithms.**Coding background:**Learning languages like R, Python to explore datasets and analyzing it goes way easier if you have a programming background. It could be C, C++, Pearl anything. That way understanding logic from a programming perspective is helpful, and hence one can easily grab the Data science stuff.**Probability & Statistics:**With a basic understanding of Probability & Statistics, this data scientist training course can be taken smoothly. This is because data science deals with many statistical tests and inferences, knowing probability and statistics will be a great boost.

## Target Audience

**Students of Analytics:**If you’re a student of data science/analytics and want your career to drift deeper into that, this data scientist training is a sure shot solution for it. This data scientist course will make you build the gap between your college curriculum and industry related to Data science by giving you practical and live experiences of projects, which deal with data manipulation, data massaging, data visualization, storytelling.**Statistician & Model Developers:**Data science deals with model generation and machine learning. If you want to fine-tune your knowledge over statistics and model generation, go ahead with this course. This data scientist training course will showcase lots of model generation use cases.**Professionals:**If you are among auditors, solution recommenders, Project managers or in a client-facing role etc., Data scientist course will be an addon and help you excel more in your career. That’s because you will able to analyze and measure every bit of your task with analytics and data science. You can impress customers with data visualization and various facts extracted from their data.

## Data Scientist Course FAQ’s

**Why should I do this data scientist course?**

This course is ideal and a great stepping stone for candidates who aspire to make their career in data science and its related tools like R, Python, Minitab, SPSS, Tableau etc. I and the professionals who deal with data, be it structured, unstructured should take this data scientist course. Wherever data comes, data science has a key role to play there. Hence, taking this course will help you grow technically.

**I don’t have a background in Data Science, can I do this data scientist course?**

The answer is partial Yes and No. Full data science is based on probability and statistics. If you understand it well, the full data scientist training will be smoother, else you might get lost in between. It would be suggestible to clear basic of probability and statistics, and then do this course

**Would this data scientist course help me in my career advancement?**

Absolutely, without any doubt. Learn from this course about data science, get your hands dirty with your rigorous and repetitive practice, and this will create golden opportunities for you. Once you will start practicing, you will find yourself fascinated by data science and its related tools. This field is exciting, and this data scientist training course will make it even more exciting. There is ample of things to explore and learn about data science in this training.

## Sample Preview

## Career Benefits

- It is a well-known fact, that dealing with varieties of datasets to bring the hidden insights, is in demand of today. All organizations today want to analyze their data so that they can make better decisions about their strategies. The person dealing with data and along with that critical thinking is something, the market is in dire need of.
- In that scenario, knowing Data science skills, great thinking and communication to make the insights reach to the audience, is a great boon.
- This data scientist training will help you kick start your career is a great way, by giving you lucid explanation about all perspective of Data Science.
- Learning data science is an advantage for your better and competitive future. Growth in job and job security is certain if you have a good hold on data science.

## Data Scientist Course Testimonials

#### Data science with python

The course goes through the different areas of data science with python. Beside a fundamental theory regarding the explained concepts, the diverse concepts are exemplified with short python programs. The lessons are good to understand and the programs presented to illustrate and implement the concepts are simple and significant.

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###### Jorge Giro

#### Business Analytics using R

The data scientist course was very comprehensive and covered in details the business analytics using R. The tutor used a clear English and explained very carefully the different topics of the course. It is very rare to find a course online that covers that much information going from the basics of statistics to the application of R to business analytics. I think this is a competitive advantage of the course since the majority of this kind of courses available online are made for people who already have knowledge about statistics or even some knowledge about the software itself, but this Data scientist course tried to cover them all which made very comfortable for me as a student.

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###### Abderrahmane Friha

#### Amazing First Sashay with Customer Analytics

If you’re looking to dipping your toes into customer analytics, this Data scientist course is a good first start. It provides the foundation knowledge for customer analytics from mapping out the customer lifecycle and drilling down on what needs to be down per phase of said lifecycle. Need to note that the data scientist training course explains from a bank / financial service standpoint, though this I feel allows the course to explain the concepts better instead of not having any examples at all.

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###### Acee Vitangcol

#### Great content

Amazing material put together, it really goes in depth with functions on Excel, R, and SAS. I really like the complexity and the level of knowledge especially the Excel area. If you are looking to analyze data on a regular basis using excel 2010, or similar platforms such as R, this training is full of great content at a very high level.

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###### Jorge Dominguez

#### Good Introduction to Marketing Analytics

It is a good Introduction to Marketing Analytics. It lists the typical activities carried out in the practice of predictive analytics. Furthermore, it helps to define how to make analytics more efficient, lists the type of models that are used in marketing and digital marketing, and some hints on the leading vendors of predictive analytics.

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###### Ricardo Garibay-Martínez,

#### Excellent

It was a well-explained course! Easy to understand and practice. Recommended for beginners.

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###### Arju Gaggar

#### Statistics Using R

The data scientist training covers t-distribution, Z statistic and Central limit theorem with exceptional clarity. It helped me in understanding the concepts with such a depth that I would never forget them now. The examples are highly practical and apt. The parallel execution of formulas in R makes it simple to remember rather than just going through the formulae, this Data Science course explains the underlying concepts first. It is the best course on R-Statistics I have come across so far.

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###### Abbas Singapurwala

#### Business Analytics using R

Business Analytics is an excellent course for beginners in both Statistics and R programming. A lot of concepts have been covered from basics. It includes descriptive analysis, regression modeling, time series forecasting. It’s an good knowledge if someone do not have any background in these techniques.

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###### Ankit Tyagi

#### Very good

Really detailed and informative course. covered everything in depth. need to know basic statistics for this Data scientist course. but this will lay a solid ground to become a data analyst. with this course realized how powerful is R software and its possibilities. Loved every bit of the data scientist training and would do more courses from EduCBA

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###### Mudassir Ahmed

#### Data scientist training

After completing Business analytics on R programming course,this was very advanced and helped me on my career towards becoming a data scientist. The data scientist training is well structured and the instructor is well organised. I would recommend this to all upcoming data scientist out there.

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###### Thakgatso Jack Dikobo

#### Best Professional Development Platform

I have tried lot of e-portals to have a complete set of curriculum on a specific professional course, but did not find any. Thanks to EduCBA for their brilliant initiative to put details of the data scientist course and related topic in a single platform along issuing a course completion certificate……

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###### SANJAY KUMAR KHILAR

#### Amazing

Very great content and easy to understand for beginners. A well structured informative course from an excellent instructor. The training start from (1) a detailed introduction with real business examples, (2)the business analytics life cycle, (3)detailed understanding of R,(4)data manipulation and statistics, (5)great examples and understanding visualization and many more.I would recommend this to anyone new or already pursuing their business analytics careers.

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###### Thakgatso Jack Dikobo

#### Business Analytics using SAS

Great course. Very knowledgeable and covers a wide variety of topics. I would highly recommend this Data Science course to anyone trying to get started with SAS as a business analytics tool. Data validation and cleaning was very interesting. The section on data manipulation and data transformation covered a wide array of topics. One main topic covered was the date types and how they are assigned in SAS, which was a bit difficult to understand at first. Thank you for the opportunity.

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###### Jonathan Johnson

#### New learning experience

I enjoyed the data scientist course and i believe the course has been a perfect way to be introduced to R programming and business analytics in general. The mix of statistics and R Programming goes to the core of the learning process. Great course very broad and helps generate further interest and awesome ideas. I am thinking of taking additional courses due to the many insights i gained from this Data Science course.

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###### AJIBOLA ADETULA

#### Advanced SAS Programming

Great course for increasing your knowledge of SAS programming. Very knowledgeable and covers a wide variety of topics. I would highly recommend this course to anyone trying to increase their SAS Programming skills. The repeating of information as it related to each topic was a bit much for me but I’m sure it could be helpful for individuals that are first starting off with the topic. This session covers MACROs in depth; including MACRO Variable and MACRO processes. Overall the data scientist course was very informative and in depth on all the topics it covered.

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###### Jonathan Johnson

#### Basics of R Review

Good course, although bothered how the text in the slides is exactly what the tutor was saying, so there could be a text format of it also for copying. Maybe there should have been more lessons about the analysis part with R, to get an impression what apx R can do with data. Overall, very useful and will help me write my thesis!

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###### Imola Fodor

#### Effective Tool for dashboard creation

This Data Science course is very informative and good for beginners. Clear, easy to understand and the instructor delivered the data scientist training in a very smooth way and covered a lot of topics. It lists the typical activities carried out in analytics and dashboard creation. Furthermore, it helps to define how to make dashboard effectively. Simple and easily demonstrated Sales and Marketing analysis, chart and table creation.

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###### Patrick

#### Nice & Refreshing Course

This course on Hadoop and HDFS Basics was nice and refreshing. Instructor has explained all the concepts in a simple way by providing sample examples. If you are completely new to hadoop and need to learn basics then I recommend you to go for this Data Science course.

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###### Almaamoon rasool abdali

#### Business Analytics Using R

This course is satisfactory for learning a few basics on R. I already have previous experience using R Studio, so there was not any new information for myself. But for someone who has no or very minimal experience with R, this Data Science course is good for teaching them the basics. People following these videos can download R and perform the same steps at the same time as the video.

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###### Conner Capdau

#### Customer Centric

This is a great data scientist training course that explains how customers relate directly with the different company departments and how each department relate to the vision of the company to satisfy the customers, I like the fact that the data scientist training its given with the assumption that the business its moving towards customer centric vision, and how customer analytics will be the key to success.

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###### Jorge Dominguez