Best Data Science Certification Online

The Complete Machine Learning Course with Python

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!

• Machine Learning Engineers earn on average $166,000 - become an ideal candidate with this course!
• Solve any problem in your business, job or personal life with powerful Machine Learning models
• Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more
• Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning etc

Introduction to Data Science

Learn how to extract information from data using data science methods, with this introduction to data science course.

This free online Introduction to Data Science course from Alison will teach you the basics of data science. You will look into data science processes, receive an introduction to machine learning, and learn about data models for structuring data. You will also be shown how to gain knowledge and insights from data that is both structured and unstructured as well as learn to use scientific methods, processes, algorithms, and data science systems.

• Describe what data science is used for
• List the stages in the data science process
• Explain what machine learning is and the parts that make it up
• Discuss the use of regression and the different types of regression
• Identify the different types of classification algorithms available for you to use
• Describe how the two most popular clustering algorithms work
• Discuss why you would use Azure ML for your data science projects

Machine Learning, Data Science and Deep Learning with Python

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

• Build artificial neural networks with Tensorflow and Keras
• Classify images, data, and sentiments using deep learning
• Make predictions using linear regression, polynomial regression, and multivariate regression
• Data Visualization with MatPlotLib and Seaborn
• Implement machine learning at massive scale with Apache Spark's MLLib
• Understand reinforcement learning - and how to build a Pac-Man bot
• Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
• Use train/test and K-Fold cross validation to choose and tune your models
• Build a movie recommender system using item-based and user-based collaborative filtering
• Clean your input data to remove outliers
• Design and evaluate A/B tests using T-Tests and P-Values

Artificial Intelligence Course Online

Take a conceptual look at Artificial Intelligence, covering topics like handling of data, preprocessing, model selection and model evaluation.

• Understand the influences of AI
• Explore the link between cognitive sciences and neural nets
• Build a simple regression problem from scratch
• Understand how ensemble learning is implemented and its benefits
• Apply machine learning algorithms to visualise data and understand feature correlation

Machine Learning with Javascript

Master Machine Learning from scratch using Javascript and TensorflowJS with hands-on projects.

• Assemble machine learning algorithms from scratch!
• Build interesting applications using Javascript and ML techniques
• Understand how ML works without relying on mysterious libraries
• Optimize your algorithms with advanced performance and memory usage profiling
• Use the low-level features of Tensorflow JS to supercharge your algorithms
• Grow a strong intuition of ML best practices

Data Analytics Certification Course

Learn to analyse data to make better decisions and gain a solid foundation in statistics to better interpret your data in this online data analytics training course.

• Basic statistical skills such as fundamental theories and terminology
• Introduction to R, data cleaning, data visualisation and packages in R that can be used for data analysis
• How to use Excel for descriptive statistics
• Introduction to Tableau
• How to interpret and present results

Diploma in Data Analytics with Python

This diploma course covers the importance and uses of data analytics in making business driven decisions in industries.

This free online data analytics course explains in great detail, analytics in a wide variety of industries. The course will be of great interest to students and professionals who wish to learn more about the use of analytics in their career and life. The course includes detail on how to develop the in-demand skills and knowledge needed to analyze data with python and drive decision-making to improve business performance in today's industries.

Basics of Python for Beginners

Join a small group of peers and an expert instructor for an engaging introduction to Python. This live online class is designed for students who have no previous experience with Python or coding. During this class, students will learn what Python is, why it is a valuable skill to learn, some basic concepts, and will get to engage with peers their own age who share an interest in computer programming and engineering. This class is the perfect starting point for early elementary students.

Data Science - Regression and Clustering Models

Learn how to create regression models, data classification models, and cluster models in Azure ML, R and Python.

This free online data science course will teach you about Regression and Clustering Models. You will look into what regression modelling and classification modelling are, look at their similarity, and learn how each of these models can be created in Azure ML, R, and Python. This course will also discuss the metrics for evaluating a classification model's performance. You will also examine unsupervised learning models, and more!

R Programming for Data Science And Machine Learning

DataQrious Academy, Make You Data Curious

R is one of the most popular and widely used tools for statistical programming. It is a powerful, versatile, and easy to use tool for data analytics, and data visualization. It is the first choice for thousands of data analysts working in both companies and academia.

• Learn to program in R at a good level and how to use R Studio
• Learn the core principles of R programming
• Learn how to create vectors in R
• Learn how to create variables
• Learn Data types in R
• Decision Making in R
• Learn how to create a while() loop and a for() loop in R
• Learn how to build and use matrices in R
• Learn how to use Functions in R
• Learn the matrix() function, learn rbind() and cbind()
• Learn to use Factors in R
• Learn to use Data Frames in R
• Learn how to install packages in R
• Learn how to use charts and Graphs in R
• Learn to read data from CSV files
• Learn Data Analysis in R
• Data Manipulation using dplyr
• Data Imputation
• Learn how to use charts in R for data visualization
• Shiny for Interactive dashboards in R
• Analysis of Covariance

Tableau 2020 A-Z: Hands-On Tableau Training for Data Science

Learn Tableau 2020 for data science step by step. Real-life data analytics exercises & quizzes included. Learn by doing!

• Install Tableau Desktop 2020
• Connect Tableau to various Datasets: Excel and CSV files
• Create Barcharts
• Create Area Charts
• Create Maps
• Create Scatterplots
• Create Piecharts
• Create Treemaps
• Create Interactive Dashboards
• Create Storylines
• Understand Types of Joins and how they work
• Work with Data Blending in Tableau
• Create Table Calculations
• Work with Parameters
• Create Dual Axis Charts
• Create Calculated Fields
• Create Calculated Fields in a Blend
• Export Results from Tableau into Powerpoint, Word, and other software
• Work with Timeseries Data (two methods)
• Creating Data Extracts in Tableau
• Understand Aggregation, Granularity, and Level of Detail
• Adding Filters and Quick Filters
• Create Data Hierarchies
• Adding Actions to Dashboards (filters & highlighting)
• Assigning Geographical Roles to Data Elements
• Advanced Data Preparation (including latest updates in Tableau)

Data Analytics - Introduction to Machine Learning

Understand machine learning and its use in data analytics with this free online introduction to machine learning course.

This free online Machine Learning course will introduce you to machine learning. Machine learning is an essential part of data analytics. With this free course, you will get up-to-date with the most important machine learning topics today. Aside from teaching you about automation, the course also covers supervised and unsupervised learning and will introduce you to important computing methods to help you find hidden information within your data.

The Ultimate Hands-On Hadoop: Tame your Big Data!

Hadoop tutorial with MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, Kafka + more! Over 25 technologies.

• Design distributed systems that manage "big data" using Hadoop and related technologies.
• Use HDFS and MapReduce for storing and analyzing data at scale.
• Use Pig and Spark to create scripts to process data on a Hadoop cluster in more complex ways.
• Analyze relational data using Hive and MySQL
• Analyze non-relational data using HBase, Cassandra, and MongoDB
• Query data interactively with Drill, Phoenix, and Presto
• Choose an appropriate data storage technology for your application
• Understand how Hadoop clusters are managed by YARN, Tez, Mesos, Zookeeper, Zeppelin, Hue, and Oozie.
• Publish data to your Hadoop cluster using Kafka, Sqoop, and Flume
• Consume streaming data using Spark Streaming, Flink, and Storm

LaTeX for Data Scientists 2021

For the class project, I want you to first choose any topic related to Data Science.

One of the most valuable skills a Data Scientist can have is to communicate correctly. Data Scientists have to provide insights to stakeholders. Remember, sometimes, we don't have to explain things with difficult math. So, the project is the following:

Write a document (keep it short) where you explain the math behind any ML or DL model to me (I am a mathematician, so you can write complex math if you want!) Now suppose I am a stakeholder. Can you explain to me how this ML or DL model will help my business to earn more money? Both approaches are important for a Data Scientist. Your written and verbal skills should be good for becoming a Data Scientists and this is a good exercise for this.

Data Analytics - Mining and Analysis of Big Data

Learn how to analyse big data using mining and clustering techniques, in this free online big data analytics course.

This free online Big Data Analytics course from Alison will teach you how to mine and analyse big data. This process of studying and evaluating data is widely used in business and commercial industries and even in the government sector. With the help of this course, you will also learn how to create clustering data models which in turn can help you make more informed decisions. Make this a part of your business practice now!

Statistics for Business Analytics and Data Science A-Z

Learn The Core Stats For A Data Science Career. Master Statistical Significance, Confidence Intervals And Much More!

• Understand what a Normal Distribution is
• Understand standard deviations
• Explain the difference between continuous and discrete variables
• Understand what a sampling distribution is
• Understand the Central Limit Theorem
• Apply the Central Limit Theorem in practice
• Apply Hypothesis Testing for Means
• Apply Hypothesis Testing for Proportions
• Use the Z-Score and Z-Tables
• Use the t-Score and t-Tables
• Understand the difference between a normal distribution and a t-distribution
• Understand and apply statistical significance
• Create confidence intervals
• Understand the potential pitfalls of overusing p-Values

Spark and Python for Big Data with PySpark

Learn how to use Spark with Python, including Spark Streaming, Machine Learning, Spark 2.0 DataFrames and more!

• Use Python and Spark together to analyze Big Data
• Learn how to use the new Spark 2.0 DataFrame Syntax
• Work on Consulting Projects that mimic real world situations!
• Classify Customer Churn with Logisitic Regression
• Use Spark with Random Forests for Classification
• Learn how to use Spark's Gradient Boosted Trees
• Use Spark's MLlib to create Powerful Machine Learning Models
• Learn about the DataBricks Platform!
• Get set up on Amazon Web Services EC2 for Big Data Analysis
• Learn how to use AWS Elastic MapReduce Service!
• Learn how to leverage the power of Linux with a Spark Environment!
• Create a Spam filter using Spark and Natural Language Processing!
• Use Spark Streaming to Analyze Tweets in Real Time!

Python A-Z™: Python For Data Science With Real Exercises!

Programming In Python For Data Analytics And Data Science. Learn Statistical Analysis, Data Mining And Visualization

• Learn to program in Python at a good level
• Learn how to code in Jupiter Notebooks
• Learn the core principles of programming
• Learn how to create variables
• Learn about integer, float, logical, string and other types in Python
• Learn how to create a while() loop and a for() loop in Python
• Learn how to install packages in Python
• Understand the Law of Large Numbers

Apache Kafka Series - Learn Apache Kafka for Beginners v2

Learn Apache Kafka 2.0 Ecosystem, Core Concepts, Real World Java Producers/Consumers & Big Data Architecture

• Understand Apache Kafka Ecosystem, Architecture, Core Concepts and Operations
• Master Concepts such as Topics, Partitions, Brokers, Producers, Consumers
• Start a personal Kafka development environment
• Learn major CLIs: kafka-topics, kafka-console-producer, kafka-console-consumer, kafka-consumer-groups, kafka-configs
• Create your Producers and Consumers in Java to interact with Kafka
• Program a Real World Twitter Producer & ElasticSearch Consumer
• Extended APIs Overview (Kafka Connect, Kafka Streams), Case Studies and Big Data Architecture
• Practice and Understand Log Compaction

Who's Who in the Data Science Team

The data scientist is often referred to as one profession, although there are other specialties in data science. We will tell you which directions you can choose from and what is required to study them.

When working in the field of data science, you must know the basics and choose one of the narrow specializations. Since data science does not have a universal understanding of them, it will not be easy to do this. Let's try to determine an approximate range of directions that should be guided by. Knowing the opportunities offered by the market will give you a head start in employment, as certain skills are required to get a specific job.

Team roles

The data goes through a series of stages before the decision maker sees a beautiful and understandable presentation. We have arranged positions in the order of their appearance in the process of working with data.

1. Data architect.

Data architecture is the rules, policies, standards and models that define the type of information collected, how it is used and stored. This includes integrating data across an organization and its systems, and addressing security and availability issues. All this is done by the data architect.

Such a specialist is critically needed in big data projects. Usually, in one company, he interacts with several teams at once, sometimes combining his main job with the role of a data engineer.

Typical tasks: along with the development of a detailed plan for the data, the specialist provides the necessary tools and systems for data engineers. In the event of any changes in the company, he understands what will happen to the data and can take measures to minimize the consequences.

What you need to know: SQL, noSQL, XML, Hive, Pig, Hadoop, Spark, machine learning, visualization, data modeling and storage, and ETL (extraction transformation and loan) and a little more.

2. Data engineer.

The knowledge of machine learning and statistics is not required for a data engineer, but it is a very important person on any team. Without them, there will be no data, and therefore no data science.

Typical tasks: These guys are responsible for receiving data, processing it and storing it. They build, test and update the IT infrastructure. Data engineers power everything a data architect has designed. Then the data scientist will have access to information and will be able to run their algorithms.

What you need to know: You will need advanced programming skills to work with large datasets and build the channels through which the data arrives. Technologies: SQL, noSQL, Hive, Pig, Matlab, SAS, Python, Java, Ruby, C ++, Perl, popular APIs and ETL tools.

Perspectives: From Junior to Head of Data Engineering.

3. Data analyst.

This role is less technical than the data scientist, although in many ways they are similar and often confused.

Typical tasks: data analysts answer questions from their colleagues, look for answers among the presented data, perform statistical analysis and translate a bunch of numbers into human language in the form of reports and visualizations. They do not predict or seek new trends on their own. Case Study: Assessing the effectiveness of a marketing campaign and how it affected sales.

What you need to know: Mathematics and statistics, numbers-based decision-making, cleaning (preparation for analysis) and data visualization techniques. You will also need an intermediate level of programming in Python or R, an average level of knowledge of SQL queries, MS Excel, SAS, Tebleau, etc.

Prospects: transition to data scientists if you want to develop in machine learning or data engineers if you are more interested in programming. Data analysts hit the salary ceiling about 10 years after joining the profession.

4. Data scientist.

Due to the lack of clearly delineated roles, some companies are looking for so-called "unicorns": specialists who are well versed in statistics, mathematics, machine learning, programming, business problems and visualization at the same time. There are such people, but there are very few of them.

The average mortal data scientist is simply more immersed in mathematics and programming than the data analyst. He has more freedom to experiment and study trends that management may not be aware of.

The data scientist walks through a sea of ​​unstructured data to identify questions and pull out information that provides the answers. All this needs to be done by understanding the business objectives. And yes, unlike a data analyst, a data scientist is also involved in predictive analysis.

The name of this role includes the word "science" for a reason. There is a process of scientific research - testing hypotheses to gain practical knowledge.

Typical tasks: defining business questions, transforming data, training and tuning machine learning models, evaluating results, predictions, reporting and visualizing by results. Example of work: predicting the likelihood of a customer canceling a subscription, clustering customers by semantic groups.

What you need to know: Scientist data analytics skills + a good understanding of machine learning methods with and without a teacher. It will require a deep understanding of statistics and the ability to evaluate statistical models, as well as more advanced programming skills.

Prospects: Transition as a Lead Data Engineer or Data Architect, Machine Learning Engineer, Lead Data Scientist (Chief Data Officer).

5. Machine Learning Engineer.

In short, a machine learning engineer is (pardon the tautology) a data scientist who specializes in machine learning.

Typical tasks: software solutions for automating ML models; design, development and testing of ML-systems. ML Engineer trains and subsequently maintains machine learning models.

What you need to know: A good understanding of statistics and mathematics is required. Technological stack: Java, Python, JS, as well as ML frameworks TensorFlow or Keras, and, of course, Hadoop or analogs.

6. Business Intelligence (BI) Developer.

The main task of BI developers is to structure and present the obtained data in an understandable form for management. They usually don't do analysis.

It is beneficial to come into this specialty with a non-technical background, because it requires a good understanding of business operations and communication.

Typical tasks: developing strategies for how other employees can effectively use the information received by Scientists and analysts; how to get the necessary information on time to make the appropriate decision. Also, a BI developer designs, creates and maintains repositories, ETL packages, dashboards and analytical reports.

Things to know: SQL, data storage, SSRS / SSAS / SSIS, ETL, Report Builder, Power BI, DAX, Tableau, dashboards, security rules, VB programming languages, C #, JavaScript, etc.

Perspectives: BI developer -> BI analyst -> BI architect -> BI manager.

7. Database administrator (DBA)

When the database is ready, someone needs to look after it. Such a specialist should be able to identify faults, quickly navigate in emergency situations and solve data-related problems.

Typical Tasks: The database administrator is responsible for backing up and restoring information, as well as security and modeling. He makes sure that everyone has access to them, everything works well, and also connects old and new databases.

What you need to know: database languages ​​(most often SQL, NoSQL), as well as the programming language that the company works with. Databases from Oracle and Microsoft, cloud services Microsoft Azure and Amazon Web Services.

Perspectives: manager of computer and information systems.