The Data Science Course: Complete Data Science Bootcamp 2025
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Part 1: Introduction
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The Field of Data Science – The Various Data Science Disciplines
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The Field of Data Science – The Benefits of Each Discipline
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The Field of Data Science – Popular Data Science TechniquesTechniques for Working with Traditional Data0sReal Life Examples of Traditional Data0sTechniques for Working with Big Data0sReal Life Examples of Big Data0sBusiness Intelligence (BI) Techniques0sReal Life Examples of Business Intelligence (BI)0sTechniques for Working with Traditional Methods0sReal Life Examples of Traditional Methods0sMachine Learning (ML) Techniques0sTypes of Machine Learning0sReal Life Examples of Machine Learning (ML)0s
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The Field of Data Science – Careers in Data Science
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The Field of Data Science – Debunking Common Misconceptions
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Part 2: Probability
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Probability – CombinatoricsFundamentals of Combinatorics0sPermutations and How to Use Them0sSimple Operations with Factorials0sSolving Variations with Repetition0sSolving Variations without Repetition0sSolving Combinations0sSymmetry of Combinations0sSolving Combinations with Separate Sample Spaces0sCombinatorics in Real-Life: The Lottery0sA Recap of Combinatorics0sA Practical Example of Combinatorics0s
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Probability – Bayesian InferenceSets and Events0sWays Sets Can Interact0sIntersection of Sets0sUnion of Sets0sMutually Exclusive Sets0sDependence and Independence of Sets0sThe Conditional Probability Formula0sThe Law of Total Probability0sThe Additive Rule0sThe Multiplication Law0sBayes’ Law0sA Practical Example of Bayesian Inference
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Probability – DistributionsFundamentals of Probability Distributions0sTypes of Probability Distributions0sCharacteristics of Discrete Distributions0sDiscrete Distributions: The Uniform Distribution0sDiscrete Distributions: The Bernoulli Distribution0sDiscrete Distributions: The Bernoulli Distribution0sDiscrete Distributions: The Poisson Distribution0sCharacteristics of Continuous Distributions0sContinuous Distributions: The Normal Distribution0sContinuous Distributions: The Standard Normal Distribution0sContinuous Distributions: The Students’ T Distribution0sContinuous Distributions: The Chi-Squared Distribution0sContinuous Distributions: The Exponential Distribution0sContinuous Distributions: The Logistic Distribution0sA Practical Example of Probability Distributions0s
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Probability – Probability in Other Fields
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Part 3: Statistics
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Statistics – Descriptive StatisticsTypes of Data0sLevels of Measurement0sCategorical Variables – Visualization Techniques0sCategorical Variables ExerciseNumerical Variables – Frequency Distribution Table0sNumerical Variables ExerciseThe Histogram0sHistogram ExerciseCross Tables and Scatter Plots0sCross Tables and Scatter Plots ExerciseMean, median and mode0sMean, Median and Mode ExerciseSkewness0sSkewness ExerciseVariance0sVariance ExerciseStandard Deviation and Coefficient of Variation0sStandard Deviation and Coefficient of Variation ExerciseCovariance0sCovariance ExerciseCorrelation CoefficientCorrelation0sCorrelation Coefficient Exercise
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Statistics – Practical Example: Descriptive Statistics
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Statistics – Inferential Statistics Fundamentals
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Statistics – Inferential Statistics: Confidence IntervalsWhat are Confidence Intervals?0sConfidence Intervals; Population Variance Known; Z-score0sConfidence Intervals; Population Variance Known; Z-score; ExerciseConfidence Interval Clarifications0sStudent’s T Distribution0sConfidence Intervals; Population Variance Unknown; T-score0sConfidence Intervals; Population Variance Unknown; T-score; ExerciseMargin of Error0sConfidence intervals. Two means. Dependent samples0sConfidence intervals. Two means. Dependent samples ExerciseConfidence intervals. Two means. Independent Samples (Part 1)0sConfidence intervals. Two means. Independent Samples (Part 1). ExerciseConfidence intervals. Two means. Independent Samples (Part 2)0sConfidence intervals. Two means. Independent Samples (Part 2). ExerciseConfidence intervals. Two means. Independent Samples (Part 3)0s
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Statistics – Practical Example: Inferential Statistics
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Statistics – Hypothesis TestingNull vs Alternative Hypothesis0sFurther Reading on Null and Alternative HypothesisNull vs Alternative Hypothesis0sRejection Region and Significance Level0sType I Error and Type II Error0sTest for the Mean. Population Variance Known0sTest for the Mean. Population Variance Known Exercisep-value0sTest for the Mean. Population Variance Unknown0sTest for the Mean. Population Variance Unknown ExerciseTest for the Mean. Dependent Samples0sTest for the Mean. Dependent Samples ExerciseTest for the mean. Independent Samples (Part 1)0sTest for the mean. Independent Samples (Part 1). ExerciseTest for the mean. Independent Samples (Part 2)0sTest for the mean. Independent Samples (Part 2). Exercise
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Statistics – Practical Example: Hypothesis Testing
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Part 4: Introduction to Python
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Python – Variables and Data Types
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Python – Basic Python Syntax
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Python – Other Python Operators
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Python – Conditional Statements
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Python – Python Functions
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Python – Sequences
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Python – Iterations
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Python – Advanced Python Tools
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Part 5: Advanced Statistical Methods in Python
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Advanced Statistical Methods – Linear Regression with StatsModels
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Advanced Statistical Methods – Multiple Linear Regression with StatsModelsMultiple Linear Regression0sAdjusted R-Squared0sMultiple Linear Regression ExerciseTest for Significance of the Model (F-Test)0sOLS Assumptions0sA1: Linearity0sA2: No Endogeneity0sA3: Normality and Homoscedasticity0sA4: No Autocorrelation0sA5: No Multicollinearity0sDealing with Categorical Data – Dummy Variables
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Advanced Statistical Methods – Linear Regression with sklearnHow are we Going to Approach this Section?0sSimple Linear Regression with sklearn0sSimple Linear Regression with sklearn – A StatsModels-like Summary Table0sA Note on NormalizationSimple Linear Regression with sklearn – ExerciseMultiple Linear Regression with sklearn0sCalculating the Adjusted R-Squared in sklearn0sCalculating the Adjusted R-Squared in sklearn – ExerciseFeature Selection (F-regression)0sA Note on Calculation of P-values with sklearnCreating a Summary Table with P-values0sMultiple Linear Regression – ExerciseFeature Scaling (Standardization)0sFeature Selection through Standardization of Weights0sPredicting with the Standardized Coefficients0sFeature Scaling (Standardization) – ExerciseUnderfitting and Overfitting0sTrain – Test Split Explained0s
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Advanced Statistical Methods – Practical Example: Linear RegressionPractical Example: Linear Regression (Part 1)0sPractical Example: Linear Regression (Part 2)0sA Note on MulticollinearityPractical Example: Linear Regression (Part 3)0sDummies and Variance Inflation Factor – ExercisePractical Example: Linear Regression (Part 4)0sDummy Variables – ExercisePractical Example: Linear Regression (Part 5)0sLinear Regression – Exercise
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Advanced Statistical Methods – Logistic RegressionIntroduction to Logistic Regression0sA Simple Example in Python0sLogistic vs Logit Function0sBuilding a Logistic Regression0sBuilding a Logistic Regression – ExerciseAn Invaluable Coding Tip0sUnderstanding Logistic Regression Tables0sUnderstanding Logistic Regression Tables – ExerciseWhat do the Odds Actually Mean0sBinary Predictors in a Logistic Regression0sBinary Predictors in a Logistic Regression – ExerciseCalculating the Accuracy of the Model0sCalculating the Accuracy of the ModelUnderfitting and Overfitting0sTesting the Model0sTesting the Model – Exercise
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Advanced Statistical Methods – Cluster Analysis
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Advanced Statistical Methods – K-Means ClusteringK-Means Clustering0sA Simple Example of Clustering0sA Simple Example of Clustering – ExerciseClustering Categorical Data0sClustering Categorical Data – ExerciseHow to Choose the Number of Clusters0sHow to Choose the Number of Clusters – ExercisePros and Cons of K-Means Clustering0sTo Standardize or not to Standardize0sRelationship between Clustering and Regression0sMarket Segmentation with Cluster Analysis (Part 1)0sMarket Segmentation with Cluster Analysis (Part 2)0sHow is Clustering Useful?0sEXERCISE: Species Segmentation with Cluster Analysis (Part 1)EXERCISE: Species Segmentation with Cluster Analysis (Part 2)
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Advanced Statistical Methods – Other Types of Clustering
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Part 6: MathematicsWhat is a Matrix?0sScalars and Vectors0sLinear Algebra and Geometry0sArrays in Python – A Convenient Way To Represent Matrices0sWhat is a Tensor?0sAddition and Subtraction of Matrices0sErrors when Adding Matrices0sTranspose of a Matrix0sDot Product0sDot Product of Matrices0sWhy is Linear Algebra Useful?0s
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Part 7: Deep Learning
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Deep Learning – Introduction to Neural NetworksIntroduction to Neural Networks0sTraining the Model0sTypes of Machine Learning0sThe Linear Model (Linear Algebraic Version)0sThe Linear Model with Multiple Inputs0sThe Linear model with Multiple Inputs and Multiple Outputs0sGraphical Representation of Simple Neural Networks0sWhat is the Objective Function?0sCommon Objective Functions: L2-norm Loss0sCommon Objective Functions: Cross-Entropy Loss0sOptimization Algorithm: 1-Parameter Gradient Descent0sOptimization Algorithm: n-Parameter Gradient Descent0s
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Deep Learning – How to Build a Neural Networks from Scratch with NumPy
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Deep Learning – TensorFlow 2.0: IntroductionHow to Install TensorFlow 2.00sTensorFlow Outline and Comparison with Other Libraries0sTensorFlow 1 vs TensorFlow 20sA Note on TensorFlow 2 Syntax0sTypes of File Formats Supporting TensorFlow0sOutlining the Model with TensorFlow 20sInterpreting the Result and Extracting the Weights and Bias0sCustomizing a TensorFlow 2 Model0sBasic NN with TensorFlow: Exercises
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Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks
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Deep Learning – Overfitting
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Deep Learning – Initialization
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Deep Learning – Digging into Gradient Descent and Learning Rate Schedules
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Deep Learning – Preprocessing
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Deep Learning – Classifying on the MNIST DatasetMNIST: The Dataset0sMNIST: How to Tackle the MNIST0sMNIST: Importing the Relevant Packages and Loading the Data0sMNIST: Preprocess the Data – Create a Validation Set and Scale It0sMNIST: Preprocess the Data – Scale the Test Data – ExerciseMNIST: Preprocess the Data – Shuffle and Batch0sMNIST: Preprocess the Data – Shuffle and Batch – ExerciseMNIST: Outline the Model0sMNIST: Select the Loss and the Optimizer0sMNIST: Learning0sMNIST – ExercisesMNIST: Testing the Model0s
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Deep Learning – Business Case ExampleBusiness Case: Exploring the Dataset and Identifying Predictors0sBusiness Case: Outlining the Solution0sBusiness Case: Balancing the Dataset0sBusiness Case: Preprocessing the Data0sBusiness Case: Preprocessing the Data – ExerciseBusiness Case: Load the Preprocessed Data0sBusiness Case: Load the Preprocessed Data – ExerciseBusiness Case: Learning and Interpreting the Result0sBusiness Case: Setting an Early Stopping Mechanism0sSetting an Early Stopping Mechanism – ExerciseBusiness Case: Testing the Model0sBusiness Case: Final Exercise
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Deep Learning – Conclusion
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Appendix: Deep Learning – TensorFlow 1: IntroductionREAD ME!!!!How to Install TensorFlow 10sA Note on Installing Packages in AnacondaTensorFlow Intro0sActual Introduction to TensorFlow0sTypes of File Formats, supporting Tensors0sBasic NN Example with TF: Inputs, Outputs, Targets, Weights, Biases0sBasic NN Example with TF: Loss Function and Gradient Descent0sBasic NN Example with TF: Model Output0sBasic NN Example with TF Exercises
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Appendix: Deep Learning – TensorFlow 1: Classifying on the MNIST DatasetMNIST: What is the MNIST Dataset?0sMNIST: How to Tackle the MNIST0sMNIST: Relevant Packages0sMNIST: Model Outline0sMNIST: Loss and Optimization Algorithm0sCalculating the Accuracy of the Model0sMNIST: Batching and Early Stopping0sMNIST: Learning0sMNIST: Results and Testing0sMNIST: ExercisesMNIST: Solutions
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Appendix: Deep Learning – TensorFlow 1: Business CaseBusiness Case: Getting Acquainted with the Dataset0sBusiness Case: Outlining the Solution0sThe Importance of Working with a Balanced Dataset0sBusiness Case: Preprocessing0sBusiness Case: Preprocessing ExerciseCreating a Data Provider0sBusiness Case: Model Outline0sBusiness Case: Optimization0sBusiness Case: Interpretation0sBusiness Case: Testing the Model0sBusiness Case: A Comment on the Homework0sBusiness Case: Final Exercise
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Software Integration
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Case Study – What’s Next in the Course?
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Case Study – Preprocessing the ‘Absenteeism_data’What to Expect from the Following Sections?Importing the Absenteeism Data in Python0sChecking the Content of the Data Set0sIntroduction to Terms with Multiple Meanings0sWhat’s Regression Analysis – a Quick RefresherUsing a Statistical Approach towards the Solution to the Exercise0sDropping a Column from a DataFrame in Python0sEXERCISE – Dropping a Column from a DataFrame in PythonSOLUTION – Dropping a Column from a DataFrame in PythonAnalyzing the Reasons for Absence0sObtaining Dummies from a Single Feature0sEXERCISE – Obtaining Dummies from a Single FeatureSOLUTION – Obtaining Dummies from a Single FeatureDropping a Dummy Variable from the Data SetMore on Dummy Variables: A Statistical Perspective0sClassifying the Various Reasons for Absence0sUsing .concat() in Python0sEXERCISE – Using .concat() in PythonSOLUTION – Using .concat() in PythonReordering Columns in a Pandas DataFrame in Python0sEXERCISE – Reordering Columns in a Pandas DataFrame in PythonSOLUTION – Reordering Columns in a Pandas DataFrame in PythonCreating Checkpoints while Coding in Jupyter0sEXERCISE – Creating Checkpoints while Coding in JupyterSOLUTION – Creating Checkpoints while Coding in JupyterExtracting the Month Value from the “Date” Column0sExtracting the Day of the Week from the “Date” Column0sEXERCISE – Removing the “Date” ColumnAnalyzing Several “Straightforward” Columns for this Exercise0sWorking on “Education”, “Children”, and “Pets”0sFinal Remarks of this Section0sA Note on Exporting Your Data as a *.csv File
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Case Study – Applying Machine Learning to Create the ‘Absenteeism_module’Standardizing the Data0sSplitting the Data for Training and Testing0sFitting the Model and Assessing its Accuracy0sInterpreting the Coefficients for Our Problem0sTesting the Model We Created0sARTICLE – A Note on ‘pickling’EXERCISE – Saving the Model (and Scaler)Preparing the Deployment of the Model through a Module
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Case Study – Loading the ‘Absenteeism_module’
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Case Study – Analyzing the Predicted Outputs in Tableau
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Appendix – Additional Python Tools
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Appendix – pandas FundamentalsIntroduction to pandas Series0sWorking with Methods in Python – Part I0sWorking with Methods in Python – Part II0sParameters and Arguments in pandas0sUsing .unique() and .nunique()0sUsing .sort_values()0sIntroduction to pandas DataFrames – Part I0sIntroduction to pandas DataFrames – Part II0spandas DataFrames – Common Attributes0sData Selection in pandas DataFrames0spandas DataFrames – Indexing with .iloc[]0spandas DataFrames – Indexing with .loc[]0s
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Bonus Lecture
*Update 2025: Intro to Data Science module updated for recent AI developments*
The Problem
Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. 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 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. (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 picture
The Solution
Data science is a multidisciplinary field. It encompasses a wide range of topics.
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Understanding of the data science field and the type of analysis carried out
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Mathematics
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Statistics
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Python
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Applying advanced statistical techniques in Python
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Data Visualization
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Machine Learning
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Deep Learning
Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. 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 2024.
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 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. Intro to Data and Data Science
Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean?
Why learn it? 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 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 the tools is the first step to doing 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. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.
3. Statistics
You need to think like a scientist before you can become a scientist. 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 trains you to think like a scientist.
4. Python
Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. 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. Tableau
Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. 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. 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, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. 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
The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow.
Why learn it?
Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines.
**What you get**
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A $1250 data science training program
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Active Q&A support
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All the knowledge to get hired as a data scientist
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A community of data science learners
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A certificate of completion
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Access to future updates
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Solve real-life business cases that will get you the job
You will become a data scientist from scratch We are happy to offer a free course. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.
What's included
- 31.5 hours on-demand video
- 132 coding exercises
- 93 articles
- 542 downloadable resources
- Access on mobile and TV
- Closed captions
- Certificate of completion