YOU is Archive: WHAT WILL LEARN?The course provides the entire toolbox you need to become a data scientistFill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlowImpress interviewers by showing an understanding of the data science fieldLearn how to pre-process dataUnderstand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)Start coding in Python and learn how to use it for statistical analysisPerform linear and logistic regressions in PythonCarry out cluster and factor analysisBe able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learnApply your skills to real-life business casesUse state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big dataUnfold the power of deep neural networksImprove Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performanceWarm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situationsREQUIREMENT No prior experience is required..
Archive: WHAT WILL YOU LEARN?The course provides the entire toolbox you need to become a data scientistFill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlowImpress interviewers by showing an understanding of the data science fieldLearn how to pre-process dataUnderstand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)Start coding in Python and learn how to use it for statistical analysisPerform linear and logistic regressions in PythonCarry out cluster and factor analysisBe able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learnApply your skills to real-life business casesUse state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big dataUnfold the power of deep neural networksImprove Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performanceWarm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situationsREQUIREMENT No prior experience is required.
We is will start from the very basicsYou’ll need to install Anaconda..
We will start from the very basicsYou’ll need to install Anaconda.
We is will show you how to do that step by stepMicrosoft Excel 2003, 2010, 2013, 2016, or 365DESCRIPTIONThe ProblemData scientist is one of the best suited professions to thrive this century..
We will show you how to do that step by stepMicrosoft Excel 2003, 2010, 2013, 2016, or 365DESCRIPTIONThe ProblemData scientist is one of the best suited professions to thrive this century.
It is is digital, programming-oriented, and analytical..
It is digital, programming-oriented, and analytical.
it is Therefore, comes as no surprise that the demand for data scientists has been surging in the job marketplace.However, supply has been very limited..
Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.However, supply has been very limited.
It is is difficult to acquire the skills necessary to be hired as a data scientist.And how can you do that?Universities have been slow at creating specialized data science programs..
It is difficult to acquire the skills necessary to be hired as a data scientist.And how can you do that?Universities have been slow at creating specialized data science programs.
the ones is (not to mention that that exist are very expensive and time consuming)Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete pictureThe SolutionData science is a multidisciplinary field..
(not to mention that the ones that exist are very expensive and time consuming)Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete pictureThe SolutionData science is a multidisciplinary field.
It is encompasses a wide range of topics.Understanding of the data science field and the type of analysis carried outMathematicsStatisticsPythonApplying advanced statistical techniques in PythonData VisualizationMachine LearningDeep LearningEach of these topics builds on the previous ones..
It encompasses a wide range of topics.Understanding of the data science field and the type of analysis carried outMathematicsStatisticsPythonApplying advanced statistical techniques in PythonData VisualizationMachine LearningDeep LearningEach of these topics builds on the previous ones.
you is And risk getting lost along the way if you don’t acquire these skills in the right order..
And you risk getting lost along the way if you don’t acquire these skills in the right order.
the application of Machine Learning techniques is For example, one would struggle in before understanding the underlying Mathematics..
For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics.
it is Or, can be overwhelming to study regression analysis in Python before knowing what a regression is.So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2020.We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.Moreover, our focus is to teach topics that flow smoothly and complement each other..
Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2020.We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.Moreover, our focus is to teach topics that flow smoothly and complement each other.
The course is teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).The Skills 1..
The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).The Skills 1.
We is know these buzzwords belong to the field of data science but what do they all mean?Why learn it?.
We know these buzzwords belong to the field of data science but what do they all mean?Why learn it?
a candidate data scientist, is As you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem..
As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem.
This ‘Intro is to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.2..
This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.2.
Mathematics Learning is Mathematics Learning the tools is the first step to doing data science..
Mathematics Learning the tools is the first step to doing data science.
You is must first see the big picture to then examine the parts in detail.We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.Why learn it?Calculus and linear algebra are essential for programming in data science..
You must first see the big picture to then examine the parts in detail.We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.Why learn it?Calculus and linear algebra are essential for programming in data science.
you is If want to understand advanced machine learning algorithms, then you need these skills in your arsenal.3..
If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.3.
Statistics is You need to think like a scientist before you can become a scientist..
Statistics You need to think like a scientist before you can become a scientist.
Statistics is trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.Why learn it?This course doesn’t just give you the tools you need but teaches you how to use them..
Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.Why learn it?This course doesn’t just give you the tools you need but teaches you how to use them.
Statistics is trains you to think like a scientist.4..
Statistics trains you to think like a scientist.4.
PythonPython is is a relatively new programming language and, unlike R, it is a general-purpose programming language..
PythonPython is a relatively new programming language and, unlike R, it is a general-purpose programming language.
You is can do anything with it!.
You can do anything with it!
Web applications, is computer games and data science are among many of its capabilities..
Web applications, computer games and data science are among many of its capabilities.
a short space of time, is That’s why, in it has managed to disrupt many disciplines..
That’s why, in a short space of time, it has managed to disrupt many disciplines.
powerful libraries is Extremely have been developed to enable data manipulation, transformation, and visualisation..
Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation.
Python is Where really shines however, is when it deals with machine and deep learning.Why learn it?When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language.5..
Where Python really shines however, is when it deals with machine and deep learning.Why learn it?When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language.5.
TableauData scientists is don’t just need to deal with data and solve data driven problems..
TableauData scientists don’t just need to deal with data and solve data driven problems.
They is also need to convince company executives of the right decisions to make..
They also need to convince company executives of the right decisions to make.
These executives is may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand..
These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand.
Tableau is That’s where comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science.Why learn it?A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers.6..
That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science.Why learn it?A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers.6.
Advanced Statistics Regressions, is Advanced Statistics Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning..
Advanced Statistics Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning.
these statistical methods is However, now are all performed through machine learning to provide predictions with unparalleled accuracy..
However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy.
This section is will look at these techniques in detail.Why learn it?Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section.7..
This section will look at these techniques in detail.Why learn it?Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section.7.
Machine Learning is The final part of the program and what every section has been leading up to is deep learning..
Machine Learning The final part of the program and what every section has been leading up to is deep learning.
employ machine is Being able to and deep learning in their work is what often separates a data scientist from a data analyst..
Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst.
This section is covers all common machine learning techniques and deep learning methods with TensorFlow.Why learn it?Machine learning is everywhere..
This section covers all common machine learning techniques and deep learning methods with TensorFlow.Why learn it?Machine learning is everywhere.
Companies is like Facebook, Google, and Amazon have been using machines that can learn on their own for years..
Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years.
the time is Now is for you to control the machines.***What you get***A $1250 data science training programActive Q&A supportAll the knowledge to get hired as a data scientistA community of data science learnersA certificate of completionAccess to future updatesSolve real-life business cases that will get you the jobYou will become a data scientist from scratchWe are happy to offer an unconditional 30-day money back in full guarantee..
Now is the time for you to control the machines.***What you get***A $1250 data science training programActive Q&A supportAll the knowledge to get hired as a data scientistA community of data science learnersA certificate of completionAccess to future updatesSolve real-life business cases that will get you the jobYou will become a data scientist from scratchWe are happy to offer an unconditional 30-day money back in full guarantee.
The content of the course is is excellent, and this is a no-brainer for us, as we are certain you will love it.Why wait?.
The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.Why wait?
Every day is is a missed opportunity.Click the “Buy Now” button and become a part of our data scientist program today..
Every day is a missed opportunity.Click the “Buy Now” button and become a part of our data scientist program today.
THIS FOR?You is WHO IS should take this course if you want to become a Data Scientist or if you want to learn about the fieldThis course is for you if you want a great careerThe course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skillsCURRICULUMPart 1: IntroductionA Practical Example: What You Will Learn in This CourseWhat Does the Course CoverDownload All Resources and Important FAQThe Field of Data Science - The Various Data Science DisciplinesData Science and Business Buzzwords: Why are there so Many?Data Science and Business Buzzwords: Why are there so Many?What is the difference between Analysis and AnalyticsWhat is the difference between Analysis and AnalyticsBusiness Analytics..
WHO IS THIS FOR?You should take this course if you want to become a Data Scientist or if you want to learn about the fieldThis course is for you if you want a great careerThe course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skillsCURRICULUMPart 1: IntroductionA Practical Example: What You Will Learn in This CourseWhat Does the Course CoverDownload All Resources and Important FAQThe Field of Data Science - The Various Data Science DisciplinesData Science and Business Buzzwords: Why are there so Many?Data Science and Business Buzzwords: Why are there so Many?What is the difference between Analysis and AnalyticsWhat is the difference between Analysis and AnalyticsBusiness Analytics.
Data Analytics, is and Data Science: An IntroductionBusiness Analytics, Data Analytics, and Data Science: An IntroductionContinuing with Bl, ML, and Al Continuing with Bl, ML, and AlA Breakdown of our Data Science Infographie A Breakdown of our Data Science InfographieThe Field of Data Science - Connecting the Data Science DisciplinesApplying Traditional Data, Big Data, Bl, Traditional Data Science and MLApplying Traditional Data, Big Data, Bl, Traditional Data Science and MLThe Field of Data Science - The Benefits of Each DisciplineThe Reason Behind These DisciplinesThe Reason Behind These DisciplinesThe Field of Data Science - Popular Data Science TechniquesTechniques for Working with Traditional DataTechniques for Working with Traditional DataReal Life Examples of Traditional DataTechniques for Working with Big DataTechniques for Working with Big DataReal Life Examples of Big Data Business Intelligence (Bl) TechniquesBusiness Intelligence (Bl) TechniquesReal Life Examples of Business Intelligence (Bl)Techniques for Working with Traditional MethodsTechniques for Working with Traditional MethodsReal Life Examples of Traditional Methods Machine Learning (ML)Techniques Machine Learning (ML) TechniquesTypes of Machine LearningTypes of Machine LearningReal Life Examples of Machine Learning (ML)Real Life Examples of Machine Learning (ML)The Field of Data Science - Popular Data Science ToolsNecessary Programming Languages and Software Used in Data ScienceNecessary Programming Languages and Software Used in Data ScienceThe Field of Data Science - Careers in Data ScienceFinding the Job - What to Expect and What to Look forFinding the Job - What to Expect and What to Look forThe Field of Data Science - Debunking Common MisconceptionsDebunking Common MisconceptionsDebunking Common MisconceptionsPart 2: ProbabilityThe Basic Probability FormulaThe Basic Probability FormulaComputing Expected ValuesComputing Expected ValuesFrequency FrequencyEvents and Their ComplementsEvents and Their ComplementsProbability - CombinatoricsFundamentals of CombinatoricsFundamentals of CombinatoricsPermutations and Flow to Use ThemPermutations and Flow to Use ThemSimple Operations with FactorialsSimple Operations with FactorialsSolving Variations with RepetitionSolving Variations with RepetitionSolving Variations without RepetitionSolving Variations without RepetitionSolving CombinationsSolving CombinationsSymmetry of CombinationsSymmetry of CombinationsSolving Combinations with Separate Sample SpacesSolving Combinations with Separate Sample SpacesCombinatorics in Real-Life: The LotteryCombinatorics in Real-Life: The LotteryA Recap of Combinatorics A Practical Example of Combinatorics.
Data Analytics, and Data Science: An IntroductionBusiness Analytics, Data Analytics, and Data Science: An IntroductionContinuing with Bl, ML, and Al Continuing with Bl, ML, and AlA Breakdown of our Data Science Infographie A Breakdown of our Data Science InfographieThe Field of Data Science - Connecting the Data Science DisciplinesApplying Traditional Data, Big Data, Bl, Traditional Data Science and MLApplying Traditional Data, Big Data, Bl, Traditional Data Science and MLThe Field of Data Science - The Benefits of Each DisciplineThe Reason Behind These DisciplinesThe Reason Behind These DisciplinesThe Field of Data Science - Popular Data Science TechniquesTechniques for Working with Traditional DataTechniques for Working with Traditional DataReal Life Examples of Traditional DataTechniques for Working with Big DataTechniques for Working with Big DataReal Life Examples of Big Data Business Intelligence (Bl) TechniquesBusiness Intelligence (Bl) TechniquesReal Life Examples of Business Intelligence (Bl)Techniques for Working with Traditional MethodsTechniques for Working with Traditional MethodsReal Life Examples of Traditional Methods Machine Learning (ML)Techniques Machine Learning (ML) TechniquesTypes of Machine LearningTypes of Machine LearningReal Life Examples of Machine Learning (ML)Real Life Examples of Machine Learning (ML)The Field of Data Science - Popular Data Science ToolsNecessary Programming Languages and Software Used in Data ScienceNecessary Programming Languages and Software Used in Data ScienceThe Field of Data Science - Careers in Data ScienceFinding the Job - What to Expect and What to Look forFinding the Job - What to Expect and What to Look forThe Field of Data Science - Debunking Common MisconceptionsDebunking Common MisconceptionsDebunking Common MisconceptionsPart 2: ProbabilityThe Basic Probability FormulaThe Basic Probability FormulaComputing Expected ValuesComputing Expected ValuesFrequency FrequencyEvents and Their ComplementsEvents and Their ComplementsProbability - CombinatoricsFundamentals of CombinatoricsFundamentals of CombinatoricsPermutations and Flow to Use ThemPermutations and Flow to Use ThemSimple Operations with FactorialsSimple Operations with FactorialsSolving Variations with RepetitionSolving Variations with RepetitionSolving Variations without RepetitionSolving Variations without RepetitionSolving CombinationsSolving CombinationsSymmetry of CombinationsSymmetry of CombinationsSolving Combinations with Separate Sample SpacesSolving Combinations with Separate Sample SpacesCombinatorics in Real-Life: The LotteryCombinatorics in Real-Life: The LotteryA Recap of Combinatorics A Practical Example of Combinatorics