Learn from Home Offer
Data Scientist Training (85 Courses, 67+ Projects)
This Data Scientist Training Course includes 85 courses, 67 Projects with 660+ 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 them. This comprehensive course will teach you programming languages, machine learning, Hadoop, Business analytics, Data Visualization and other important analytics concepts.
BESTSELLER
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Python Data Scientist Courses (18 Courses)
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Python Data Scientist Projects (6 Projects)
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R Programming Data Scientist Courses (12 Courses)
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R Programming Data Scientist Projects (11 Projects)
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SAS Data Scientist Courses (9 Courses)
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* One Time Payment & Get Lifetime Access
* One Time Payment & Get Lifetime Access
What you get in this Data Scientist Training?
Online Classes
Technical Support
Mobile App Access
Case Studies
About Data Scientist Course
| Course | No. of Hours | |
|---|---|---|
| Machine Learning with Python 2022 | 5h 31m | |
| Project on Machine Learning - Covid19 Mask Detector | 2h 1m | |
| Machine Learning Project - Auto Image Captioning for Social Media | 2h 27m | |
| Machine Learning with SciKit-Learn in Python | 8h 15m | |
| Machine Learning Python Case Study - Predictive Modeling | 7h 1m | |
| Matplotlib for Python Developers - Beginners | 4h 24m | |
| Matplotlib for Python Developers - Intermediate | 2h 55m | |
| Matplotlib for Python Developers - Advanced | 5h 12m | |
| Pandas with Python Tutorial | 5h 6m | |
| NumPy and Pandas Python | 5h 07m | |
| Pandas Python Case Study - Data Management for Retail Dataset | 3h 35m | |
| Python Case Study - Sentiment Analysis | 1h 08m | |
| Data Science with Python | 4h 22m | |
| Artificial Intelligence with Python - Beginner Level | 2h 55m | |
| Artificial Intelligence with Python - Intermediate Level | 4h 43m | |
| AI Artificial Intelligence with Python | 6h 36m | |
| Video Analytics using OpenCV and Python Shells | 2h 28m | |
| Machine Learning using Python | 3h 32m | |
| Statistics Essentials with Python | 3h 26m | |
| 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 31m | |
| Seaborn Python - Beginners | 2h 32m | |
| Seaborn Python - Intermediate | 1h 22m | |
| Seaborn Python - Advanced | 1h 58m | |
| PySpark Python - Beginners | 2h 33m | |
| PySpark Python - Intermediate | 2h 1m | |
| PySpark Python - Advanced | 0m | |
| Machine Learning Python Case Study - Diabetes Prediction | 1h 05m | |
| R Studio UI and R Script Basics | 4h 14m | |
| R Programming for Data Science | A Complete Courses to Learn | 6h 22m | |
| R Practical - Logistic Regression with R | 4h 18m | |
| Project - Decision Tree Modeling using R | 1h 44m | |
| Project on ML - Churn Prediction Model using R Studio | 1h 25m | |
| Financial Analytics in R - Beginners | 3h 53m | |
| Financial Analytics in R - Intermediate | 1h 25m | |
| Financial Analytics in R - Advanced | 1h 4m | |
| R for Finance - Beginners to Beyond | 2h 18m | |
| Comprehensive Course on R | 4h 1m | |
| Project on R - Forecasting using R | 4h 41m | |
| 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 | |
| Case Study - Customer Analytics using Tableau and R | 2h 11m | |
| Case Study - Pricing Analytics using Tableau and R | 2h 02m | |
| Business Analytics using R - Hands-on! | 16h 11m | |
| Project - Market Basket Analysis in R | 39m | |
| Project - Hypothesis Testing using R | 3h 11m | |
| Data Visualization with R Shiny - The Fundamentals | 44m | |
| Data Science with R | 6h 15m | |
| R Studio Anova Techniques Course | 2h 18m | |
| SAS Business Analytics for Beginners | 10h 59m | |
| Predictive Modeling with SAS Enterprise Miner | 9h 35m | |
| Quantitative Finance with SAS | 3h 32m | |
| SAS Statistics | 8h 33m | |
| SAS ODS (Output Delivery System) | 9h 09m | |
| SAS PROC SQL | 14h 14m | |
| SAS Macros | 7h 38m | |
| SAS Advanced Analytics | 12h 34m | |
| SAS Graph | 2h 12m | |
| SAS DS2 | 5h 2m | |
| SAS SQL | 3h 03m | |
| SAS Practical - Macros | 5h 12m | |
| SAS Advanced Programming | 11h 28m | |
| 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 39m | |
| MapReduce - Advanced | 5h 01m | |
| Hive - Beginners | 2h 55m | |
| Hive - Advanced | 5h 16m | |
| PIG - Beginners | 2h 15m | |
| PIG - Advanced | 2h 14m | |
| NoSQL Fundamentals | 2h 04m | |
| Mahout | 3h 55m | |
| Apache Oozie | 2h 18m | |
| Apache Flume | 20m | |
| Apache Storm | 2h 7m | |
| 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 2022 - Beginners | 1h 07m | |
| SPSS 2022 - Advanced | 5h 28m | |
| Advanced SPSS Project: Impact of EMI on Home Loan | 43m | |
| Advanced SPSS Project: Impact of Total Turnover in Equity Market | 59m | |
| Advanced SPSS Project: Impact of Trade Data in Equity Market | 46m | |
| SPSS GUI and Applications | 1h 16m | |
| SPSS - Correlation Techniques | 2h 25m | |
| SPSS - Linear Regression Modeling | 3h 16m | |
| SPSS - Multiple Regression Modeling | 2h 35m | |
| SPSS - Logistic Regression | 2h 04m | |
| SPSS - Multinomial Regression | 2h 2m | |
| SPSS GUI for Statistical Analysis | 2h 08m | |
| Tableau Desktop Training 2022 | 4h 25m | |
| Tableau Project-Creating Dashboard and Stories For Financial Markets | 2h 12m | |
| Tableau Project - Russo-Ukraine War A Data Analytical Review | 1h 03m | |
| Tableau | 5h 21m | |
| BI Tools and Tableau Analytics | 5h 48m | |
| Business Intelligence with Tableau | 5h 5m | |
| Tableau Features Hands-on! | 5h 47m | |
| Tableau Practical - Super Store Business Requirements | 46m | |
| Analytics using Tableau | 9h 03m | |
| Minitab for Beginners - 2022 | 1h 12m | |
| Advanced Minitab Training - 2022 | 4h 06m | |
| Minitab Practical - Impact of Predictors on Response | 1h 38m | |
| Statistical Analysis using Minitab - Beginners to Beyond | 4h 29m | |
| Minitab GUI and Descriptive Statistics | 2h 48m | |
| 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 43m | |
| Logistic Regression using SAS Stat | 0m | |
| Linear Regression in Python | 2h 31m | |
| Python Data Science Case Study - Predicting Survival of Titanic Passengers | 1h 51m | |
| Project on R - Card Purchase Prediction | 2h 31m | |
| Machine Learning Python Case Study - Develop Movie Recommendation Engine | 52m | |
| R Practical - Employee Attrition Prediction using Random Forest Technique and R | 2h 2m | |
| Project on Term Deposit Prediction using Logistic Regression CART Algorithm | 1h 42m | |
| Poisson Regression with SAS Stat | 2h 26m | |
| Machine Learning Project using Caret in R | 1h 6m | |
| Time Series Analysis in Python - Sales Forecasting | 2h 18m |
| Course Name | Online Data Scientist Course Bundle |
| Deal | You get access to all 85 courses, 67 Projects bundle. You do not need to purchase each course separately |
| Hours | 660+ 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 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 85 courses, 67 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 to solve real organizational problems. 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’ perspective up to the advanced ones. The questions included in the Hadoop module will be explained by the educator in detail and in almost all of the scenarios the trainer 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 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 work 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 to 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.
Industry Growth Trend
The overall data science platform market is expected to grow from USD 19.58 billion in 2016 to USD 101.37 billion by 2021, at a CAGR of 38.9% from 2016 to 2021.[Source - MarketsandMarkets]
Average Salary
Average Salary$120,301 per year
The average salary for a Data Scientist is $120,301 per year in the United States.
[Source - Indeed]
[Source - Indeed]
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 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., the 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. Besides 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.
Linked
Jorge Giro
Business Analytics using R
The data scientist course was very comprehensive and covered in detail the business analytics using R. The tutor used 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 know 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.
Linked
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 the said lifecycle. I 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.
Linked
Acee Vitangcol
Great content
Amazing material put together, it goes in-depth with functions on Excel, R, and SAS. I like the complexity and the level of knowledge especially the Excel area. If you are looking to analyze data regularly using excel 2010, or similar platforms such as R, this training is full of great content at a very high level.
Linked
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.
Linked
Ricardo Garibay-Martínez,
Excellent
It was a well-explained course! Easy to understand and practice. Recommended for beginners.
Linked
Arjun 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.
Linked
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 good knowledge if someone does not have any background in these techniques.
Linked
Ankit Tyagi
Very good
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
Linked
Mudassir Ahmed
Data scientist training
After completing Business analytics on the R programming course, this was very advanced and helped me with my career towards becoming a data scientist. The data scientist training is well structured and the instructor is well organized. I would recommend this to all upcoming data scientists out there.
Linked
Thakgatso Jack Dikobo
Best Professional Development Platform
I have tried a 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……
Linked
SANJAY KUMAR KHILAR
Amazing
Very great content and easy to understand for beginners. A well structured informative course from an excellent instructor. The training starts 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.
Linked
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.
Linked
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.
Linked
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 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.
Linked
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 of what apx R can do with data. Overall, very useful, and will help me write my thesis!
Linked
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 a dashboard effectively. Simple and easily demonstrated Sales and Marketing analysis, chart, and table creation.
Linked
Patrick
Nice & Refreshing Course
This course on Hadoop and HDFS Basics was nice and refreshing. The instructor has explained all the concepts simply 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.
Linked
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.
Linked
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 it is moving towards the customer-centric vision, and how customer analytics will be the key to success.
Linked
