In this practical hands-on course, the main objective is to give you foundational education on using Python Basics and Advanced Functions. Although, surely, you understand and are aware that theory is important to build a solid foundation, that theory alone isn’t going to get the job done, so that’s why this course is packed with practical hands-on examples that you can follow step by step. This section gives you a full introduction to basic Python and advanced data types with hands-on, step-by-step training.
- Access 36 lectures & 3 hours of content 24/7
- Know the basic & advanced types of Python
- Learn about integers, floats, & complex numbers
- Understand strings & operators
- Explore Python’s lists, tuplets, sets &, dictionary
The Machine Learning & Data Science Developer Certification Program provides a comprehensive set of knowledge and skills in data science, machine learning, and deep learning. This immersive training curriculum covers all the key technologies, techniques, principles, and practices you need to play a key role in your data science development team and distinguish yourself professionally. This program moves progressively and rapidly to cover the foundational components at the core of machine learning, beginning with foundational principles and concepts used in data science and machine learning.
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- Access 64 lectures & 11 hours of content 24/7
- Develop to real-world machine learning problems
- Explain & discuss the essential concepts of machine learning and, in particular, deep learning
- Implement supervised & unsupervised learning models for tasks such as forecasting, predicting and outlier detection
- Apply & use advanced machine learning applications, including recommendation systems and natural language processing
- Evaluate & apply deep learning concepts and software applications
- Identify, source & prepare raw data for analysis and modelling
- Work with open source tools such as Python, Scikit-learn, Keras and Tensorflow
Fully equip yourself with the art of applied machine learning using MATLAB. This course is also for you if you want to apply the most commonly used data preprocessing techniques without learning all the complicated maths. Additionally, this course is also for you if you have had previous hours and hours of machine learning implementation but could never figure out how to further improve the performance of the machine learning algorithms. By the end of this course, you will have at your fingertips a wide variety of most commonly used data preprocessing techniques that you can use instantly to maximize your insight into your data set.
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- Access 26 lectures & 4 hours of content 24/7
- Implement different machine learning classification algorithms using MATLAB
- Proprocess data before analysis
- Know when & how to use dimensionality reduction
- Take away code templates
- See visualization results of algorithms
- Decide which algorithm to choose for your dataset
Programming is one of the most flexible fields I know of. You can create a program that achieves a certain task in so many ways. However, that does not mean that all ways are equal. Some are better than others. For example, you can create a program that achieves the same task as the other, but it does so 1000 times faster. It all depends on how you code and which coding practices you use. And this is what you will learn here. You will learn the good and the bad coding practices to code the right way when dealing with big data. This 100% project-based course will use Python, the Numpy, and the Moviepy library to create a fully functional sound processing program.
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- Access 56 lectures & 7 hours of content 24/7
- Learn about code optimization in Python using the NumPy library
- Understand sound processing in Python using the MoviePy library
- Know the fundamentals of digital images
- Create a program that achieves the same task like others
- Learn the good & the bad coding practices
Flume and Sqoop are important elements of the Hadoop ecosystem, transporting data from sources like local file systems to data stores. This is an essential component to organizing and effectively managing Big Data, making Flume and Sqoop great skills to set you apart from other data analysts.
- Access 16 lectures & 2 hours of content 24/7
- Use Flume to ingest data to HDFS & HBase
- Optimize Sqoop to import data from MySQL to HDFS & Hive
- Ingest data from a variety of sources including HTTP, Twitter & MySQL
Big Data describes the methodology used by major and minor corporations alike to manage and derive insight from enormous amounts of data. Some of the most important tools for working with Big Data are Hadoop, Spark, Apache Storm, and QlikView, all of which you’ll learn in detail over this course.
- Access 120 lectures & 17 hours of content 24/7
- Install Hadoop in standalone, pseudo-distributed, & fully distributed modes
- Customize your MapReduce jobs
- Learn how to leverage the power of TDDs & data frames to manipulate data w/ ease in Spark
- Understand the building blocks of every Apache
- Storm topology: Spouts & Bolts
- Run a Storm topology in the local mode & the remote mode
- Cover the Qlikview In-memory data model
- Use list boxes, table boxes, & chart boxes to query data in Qlikview
Start a career in data science by learning how to combine your Excel knowledge with Python programming, machine learning, and data science. Then, take your spreadsheets to the next level by learning the essentials of coding specifically tailored for data science in Excel. Completely tailored for beginners, this is a life-changing course you don’t want to miss. At the end of this course, you will have real-world apps to use in your portfolio.
- Access 46 lectures & 4 hours of content 24/7
- Understand basic machine learning concepts
- Get a quick introduction to Python
- Project track stocks in Excel
- Explore linear regression on stock data in Excel
This course does not require any previous Data Science experience. The goal of ‘Data Science for Beginners’ is to get you acquainted with Data Science methodology, data science concepts, programming languages, give you a peek into how machine learning works, and finally show you a data science tool like GitHub, which lets you collaborate with your colleagues. Going through the methodology is meant to introduce concepts, not prepare you to apply them fully. You will get a chance to do this in other courses (ours or other providers).
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- Access 35 lectures & 2 hours of content 24/7
- Explain the key concepts in data science: big data, data mining, libraries, datasets, API’s
- Learn about Programming languages & which ones to learn
- Understand Data Science Methodology expressed via Healthcare Insurance Company Case Study
- Experience The Power of Machine Learning & Natural Language Processing via Chatbot Example
- Know more about GitHub, how to use it for collaboration & version control
In this practical hands-on course, you’ll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data practically. In addition, you will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The main objective is to give you knowledge and understanding of the ins and outs of the R programming language and learn exactly how to become a professional Data Scientist with R and land your first job.
- Access 64 lectures & 20 hours of content 24/7
- Learn how to program R & use it for effective data analysis and visualization
- Make use of data in a practical manner
- Install & configure software necessary for a statistical programming environment
- Describe generic programming language concepts as they are implemented in a high-level statistical language
- Enderstand the ins & outs of the R programming language
- Become a professional Data Scientist with R and land your first job
Welcome to the Starting a Data Science Career with Python course. In this practical hands-on course, the main objective is to provide you with the fundamentals of starting a data science career with python. Although you surely know and understand that theory is important to build a solid foundation, that theory alone is not going to get the job done, so that’s why this course is packed with practical, hands-on examples that you can follow step by step.
- Access 14 lectures & 1 hour of content 24/7
- Build a brand using Python
- Learn what personal branding is & how it affects your career in data science
- Know more about freelancing & freelance websites
- Understand networking
- Create a resume
- learn about python industry, job opportunities, & the marketplace
Welcome to the Mathematics for Data Science course. In this practical hands-on course, the main objective is to provide you with foundational education regarding Mathematics for Data Science. As we all know and understand that theory is important to build a solid foundation, that theory alone will not get the job done; that’s why this course is packed with practical, hands-on examples that you can follow step by step.
- Access 12 lectures & 1 hour of content 24/7
- Know more about descriptive statistics
- Understand the measure of variability
- Understand inferential statistics
- Learn about probability & hypothesis testing
In this practical, hands-on course, you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to use that data practically. The main objective is to educate you to understand the Python programming language’s ins and outs for Data Science and Machine Learning and learn exactly how to become a professional Data Scientist with Python and land your first job.
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- Access 115 lectures & 19 hours of content 24/7
- Learn data cleaning, processing, wrangling, & manipulation
- Create a resumé & land your first job as a data scientist
- Use Python for Data Science
- Write complex Python programs for practical industry scenarios
- Learn Plotting in Python (graphs, charts, plots, histograms, & more)
“I think the course is very well explained, the presenter does a good emphasis on important points. And having as an introduction to the course how someone needs to approach a job interview is a fantastic idea, as it makes your brain more focused and aimed-oriented. Good job.” – Alvaro Paz Navas
In this practical hands-on course, the main objective is to give you the foundational education on Python Scripting and Libraries. Although you surely understand and are aware that theory is important to build a solid foundation, that theory alone isn’t going to get the job done, so that’s why this course is packed with practical hands-on examples that you can follow step by step. This section gives you a full introduction to the scripting and libraries with hands-on, step-by-step training.
- Access 33 lectures & 1 hour of content 24/7
- Understand Python scripting, libraries & OOP
- Know about Python IDEs, text editors & others
- Learn more about third-party libraries, numpy + pandas, & data visualization
- Explore web scraping, OOP key defitions, Python decorator, & more
This Python for Data Science course introduces Python and how to apply it in data science. Starting with some fundamentals about “what is data science” and “which is a data scientist,” the program rapidly moves into the specific challenges of data science. This includes problem definitions and collecting data, data pipelines, data preparation, data cleaning, and related subjects.
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- Access 71 lectures & 9 hours of content 24/7
- Explain machine learning & its technologies
- Discuss & apply Python fundamentals
- Understand the NumPy package
- Use data analysis using Pandas & data visualization
- Implement supervised (regression and classification) & unsupervised (clustering) machine learning
- Describe the behavior of data in Python models
- Understand how to use the various Python libraries to manipulate data, like Numpy, Pandas & Scikit-Learn
- Use Python libraries & work on data manipulation, data preparation and data explorations
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