Advanced Data Science with IBM

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Product is rated as #63 in category Data Science
Learner rating8.9
Content rating8.5

Become an IBM-approved Expert in Data Science, Machine Learning and Artificial Intelligence. Proven deep understanding on massive parallel data processing.

Last updated on March 15, 2021 8:22 pm

$49.00

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  • Coursera
  • IBM
  • Microdegree
  • Self-paced
  • Advanced
  • 3+ Months
  • Paid Course (Paid certificate)
  • English
  • Apache System Big data Data Science Data Science with 'Python' Deep learning Keras Machine learning Pytorch Spark TensorFlow

About this Course

Expert in Data Science, Machine Learning and AI. Become an IBM-approved Expert in Data Science, Machine Learning and Artificial Intelligence. After you complete this Advanced Data Science with IBM specializaiton,  you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning.

You’ll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability.

If you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

Syllabus

There are 4 Courses in this Specialization

Course 1. Fundamentals of Scalable Data Science

Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models.

Show more

In this course you will learn the fundamentals of Apache Spark using python and pyspark. We’ll introduce Apache Spark in the first two weeks and learn how to apply it to compute basic exploratory and data pre-processing tasks in the last two weeks. Through this exercise you’ll also be introduced to the most fundamental statistical measures and data visualization technologies.
This gives you enough knowledge to take over the role of a data engineer in any modern environment. But it gives you also the basis for advancing your career towards data science.

Please have a look at the full specialization curriculum:
Fundamentals of Scalable Data Science
If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

After completing this course, you will be able to:

Show more
  • Describe how basic statistical measures, are used to reveal patterns within the data
  • Recognize data characteristics, patterns, trends, deviations or inconsistencies, and potential outliers.
  • Identify useful techniques for working with big data such as dimension reduction and feature selection methods
  • Use advanced tools and charting libraries to:
    • Improve efficiency of analysis of big-data with partitioning and parallel analysis
    • Visualize the data in an number of 2D and 3D formats (Box Plot, Run Chart, Scatter Plot, Pareto Chart, and Multidimensional Scaling)

Prerequisites:

Show more
  • Basic programming skills in python
  • Basic math
  • Basics in SQL (you can get it easily from https://www.coursera.org/learn/sql-data-science if needed)

In order to complete this course, the following technologies will be used:

These technologies are introduced in the course as necessary so no previous knowledge is required.

  • Jupyter notebooks (brought to you by IBM Watson Studio for free)
  • ApacheSpark (brought to you by IBM Watson Studio for free)
  • Python

Course 2. Advanced Machine Learning and Signal Processing

The Advanced Machine Learning and Signal Processing course is part of the IBM Advanced Data Science Specialization which IBM is currently creating and gives you easy access to the invaluable insights into Supervised and Unsupervised Machine Learning Models used by experts in many field relevant disciplines.

Show more

We’ll learn about the fundamentals of Linear Algebra to understand how machine learning modes work. Then we introduce the most popular Machine Learning Frameworks for python Scikit-Learn and SparkML. SparkML is making up the greatest portion of this course since scalability is key to address performance bottlenecks. We learn how to tune the models in parallel by evaluating hundreds of different parameter-combinations in parallel.

We’ll continuously use a real-life example from IoT (Internet of Things), for exemplifying the different algorithms. For passing the course you are even required to create your own vibration sensor data using the accelerometer sensors in your smartphone. So you are actually working on a self-created, real dataset throughout the course.

If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

Course 3. Applied AI with DeepLearning

This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines.

Show more

We’ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular Deep Learning Frameworks like Keras, TensorFlow, PyTorch, Deep Learning 4J and Apache System ML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs.

IMPORTANT: THIS COURSE ALONE IS NOT SUFFICIENT TO OBTAIN THE “IBM Watson IoT Certified Data Scientist certificate”. You need to take three other courses where two of them are currently built

Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of Deep Learning models. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. This course will help serve as your guide, if you’ve ever wanted to become better at anything,

Prerequisites for Advanced Machine Learning and Signal Processing:

Show more

Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well.

If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

Course 4. Advanced Data Science Capstone

This project completer has proven a deep understanding on massive parallel data processing, data exploration and visualization, advanced machine learning and deep learning and how to apply his knowledge in a real-world practical use case where he justifies architectural decisions, proves understanding the characteristics of different algorithms, frameworks and technologies and how they impact model performance and scalability.

Please note: You are requested to create a short video presentation at the end of the course. This is mandatory to pass. You don’t need to share the video in public.

Project Advanced Data Science with IBM

Learners will build fully scalable end to end data integration, machine learning and deep learning pipelines using the most prominent and widely used frameworks and technologies like Apache Spark, scikit-learn, Spark ML, System ML, TensorFlow, Keras, PyTorch, Deep Learning4J, Apache CouchDB and MQTT.

Review on Advanced Data Science with IBM Specialization

It has been reported that some of the material in this course is too advanced. So in case you feel the same, please have a look at the following materials first before starting this course, we’ve been reported that this really helps.

Of course, you can give this course a try first and then in case you need, take the following courses / materials. It’s free…

  1. Learn Spark
  2. Spark lab basic concepts

Note: Your review matters

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Expert Score
8.9
Learner rating
8.9
Content rating
8.5
PROS: Great Introductory course for Big Data Analytics Exercises and assignments have the appropriate level of difficulty Interesting concepts with more math Go deep in machine learning and signal processing with different datasets Worthy course programs for the price offered
CONS: The concepts ain't explained completely here (being advanced course) The videos need to be updated

Description

About this Course

Expert in Data Science, Machine Learning and AI. Become an IBM-approved Expert in Data Science, Machine Learning and Artificial Intelligence. After you complete this Advanced Data Science with IBM specializaiton,  you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning.

You’ll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability.

If you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

Syllabus

There are 4 Courses in this Specialization

Course 1. Fundamentals of Scalable Data Science

Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models.

Show more

In this course you will learn the fundamentals of Apache Spark using python and pyspark. We’ll introduce Apache Spark in the first two weeks and learn how to apply it to compute basic exploratory and data pre-processing tasks in the last two weeks. Through this exercise you’ll also be introduced to the most fundamental statistical measures and data visualization technologies.
This gives you enough knowledge to take over the role of a data engineer in any modern environment. But it gives you also the basis for advancing your career towards data science.

Please have a look at the full specialization curriculum:
Fundamentals of Scalable Data Science
If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

After completing this course, you will be able to:

Show more
  • Describe how basic statistical measures, are used to reveal patterns within the data
  • Recognize data characteristics, patterns, trends, deviations or inconsistencies, and potential outliers.
  • Identify useful techniques for working with big data such as dimension reduction and feature selection methods
  • Use advanced tools and charting libraries to:
    • Improve efficiency of analysis of big-data with partitioning and parallel analysis
    • Visualize the data in an number of 2D and 3D formats (Box Plot, Run Chart, Scatter Plot, Pareto Chart, and Multidimensional Scaling)

Prerequisites:

Show more
  • Basic programming skills in python
  • Basic math
  • Basics in SQL (you can get it easily from https://www.coursera.org/learn/sql-data-science if needed)

In order to complete this course, the following technologies will be used:

These technologies are introduced in the course as necessary so no previous knowledge is required.

  • Jupyter notebooks (brought to you by IBM Watson Studio for free)
  • ApacheSpark (brought to you by IBM Watson Studio for free)
  • Python

Course 2. Advanced Machine Learning and Signal Processing

The Advanced Machine Learning and Signal Processing course is part of the IBM Advanced Data Science Specialization which IBM is currently creating and gives you easy access to the invaluable insights into Supervised and Unsupervised Machine Learning Models used by experts in many field relevant disciplines.

Show more

We’ll learn about the fundamentals of Linear Algebra to understand how machine learning modes work. Then we introduce the most popular Machine Learning Frameworks for python Scikit-Learn and SparkML. SparkML is making up the greatest portion of this course since scalability is key to address performance bottlenecks. We learn how to tune the models in parallel by evaluating hundreds of different parameter-combinations in parallel.

We’ll continuously use a real-life example from IoT (Internet of Things), for exemplifying the different algorithms. For passing the course you are even required to create your own vibration sensor data using the accelerometer sensors in your smartphone. So you are actually working on a self-created, real dataset throughout the course.

If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

Course 3. Applied AI with DeepLearning

This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines.

Show more

We’ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular Deep Learning Frameworks like Keras, TensorFlow, PyTorch, Deep Learning 4J and Apache System ML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs.

IMPORTANT: THIS COURSE ALONE IS NOT SUFFICIENT TO OBTAIN THE “IBM Watson IoT Certified Data Scientist certificate”. You need to take three other courses where two of them are currently built

Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of Deep Learning models. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. This course will help serve as your guide, if you’ve ever wanted to become better at anything,

Prerequisites for Advanced Machine Learning and Signal Processing:

Show more

Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well.

If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

Course 4. Advanced Data Science Capstone

This project completer has proven a deep understanding on massive parallel data processing, data exploration and visualization, advanced machine learning and deep learning and how to apply his knowledge in a real-world practical use case where he justifies architectural decisions, proves understanding the characteristics of different algorithms, frameworks and technologies and how they impact model performance and scalability.

Please note: You are requested to create a short video presentation at the end of the course. This is mandatory to pass. You don’t need to share the video in public.

Project Advanced Data Science with IBM

Learners will build fully scalable end to end data integration, machine learning and deep learning pipelines using the most prominent and widely used frameworks and technologies like Apache Spark, scikit-learn, Spark ML, System ML, TensorFlow, Keras, PyTorch, Deep Learning4J, Apache CouchDB and MQTT.

Review on Advanced Data Science with IBM Specialization

It has been reported that some of the material in this course is too advanced. So in case you feel the same, please have a look at the following materials first before starting this course, we’ve been reported that this really helps.

Of course, you can give this course a try first and then in case you need, take the following courses / materials. It’s free…

  1. Learn Spark
  2. Spark lab basic concepts

Note: Your review matters

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Specification:

  • Coursera
  • IBM
  • Microdegree
  • Self-paced
  • Advanced
  • 3+ Months
  • Paid Course (Paid certificate)
  • English
  • Apache System Big data Data Science Data Science with 'Python' Deep learning Keras Machine learning Pytorch Spark TensorFlow

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Advanced Data Science with IBM
Advanced Data Science with IBM

$49.00

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