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..., GitHub
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......
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Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!
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.
Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks
Take a conceptual look at Artificial Intelligence, covering topics like handling of data, preprocessing, model selection and model evaluation.
Master Machine Learning from scratch using Javascript and TensorflowJS with hands-on projects.
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.
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.
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.
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!
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 Tableau 2020 for data science step by step. Real-life data analytics exercises & quizzes included. Learn by doing!
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.
Hadoop tutorial with MapReduce, HDFS, Spark, Flink, Hive, HBase, MongoDB, Cassandra, Kafka + more! Over 25 technologies.
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.
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!
Learn The Core Stats For A Data Science Career. Master Statistical Significance, Confidence Intervals And Much More!
Learn how to use Spark with Python, including Spark Streaming, Machine Learning, Spark 2.0 DataFrames and more!
Programming In Python For Data Analytics And Data Science. Learn Statistical Analysis, Data Mining And Visualization
Learn Apache Kafka 2.0 Ecosystem, Core Concepts, Real World Java Producers/Consumers & Big Data Architecture
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.
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.
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.
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.
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.
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).
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.
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.
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.
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