Welcome to Data Science in 10 Days!. Hopefully, you have completed Python in 10 Days as this follows from Python in 10 Days.
This Data Science in 10 Days is specially designed or Non-Tech professionals. This is because we believe that Data Science skills is an essential skill for everyone at this time. So I recommend, you follow the video lessons as soon as a new Day is published.
Feel free to mention if you have any challenges. Ensure to subscribe so you get notified when a new lesson is released.
Complete Python Tutorials
Day 1 – Introduction/Review of Python/R, Jupyter Notebook, Anaconda Navigator |
- Concept of Data Science and Types of Data
- Review of Python and Jupyter Notebook
- Adding Modules using Anaconda Navigator
- Setting up and Using R Studio
- Rewriting Arithmetic Expressions
| Link to Video will be provided here. |
Day 2 – Data Acquisition and Preparation (Various Sources of Free Datasets) |
- Getting Datasets from R
- Exporting from R
- Importing to Python (.txt, .xls, .xlsx,.data and others)
- Getting Dataset From Machine Learning Repository
- Getting Dataset from MS Azure ML Studio
- Generating a Custom Dataset using Python Functions
- Python Dictionary, Lists, Tuples and Sets
- Numpy Arrays, Matrices and Pandas DataFrame
- Creating a Pandas DataFrame
| Link to Video will be provided here. |
Day 3 – Plotting and Data Visualization |
- Introduction and Basics of Plotting
- Formatting Your Plot
- Formatting Your Plot Using shorthand
- Matplotlib.Pyplot Functions
- Plotting the Heart Curve and the Figure 8 Shape
- Working with Subplots
- Creating as Scatterplot
- Creating a Histogram
- Tutorial 10 – Creating a Bar Chart
- Using a Heatmap
- Creating a Pairplot (Scatter Matrix)
| Link to Video will be provided here. |
Day 4 – Regression Analysis I, Regression Analysis II |
- Types of Regression
- Deducing the Equation of a Regression Line
- Linear Regression Demo in Python
- Polynomial Regression
- Multiple Regression
- Making Inference and Prediction
- Discreet and continuous variable
- Quantitative and Qualitative Data
| Link to Video will be provided here. |
Day 5 – Logistic Regression and Classification |
- Basics of Logistic Regression
- The Logistic Function
- Odds and Odds ratio
- Overview of Some Statistical Concepts
- Performing Classification on Discreet data
- A little about Probability Theory
| Link to Video will be provided here. |
Day 6 – Decision Trees, Building Classifiers |
- Basics of Decision Trees
- Decision Trees for Classification and Regression
- Concept of Pruning
- Demo: Building a Decision Tree Classifier
- The Bayes’ Classifier
- K-Nearest Neighbor Classifier
| Link to Video will be provided here. |
Day 7 – Factor Analysis and Principal Components Analysis |
- Introduction to PCA
- Introduction to Factor Analysis
- Test for Sampling Adequacy
- The KMO Statistics
- Concept of Scores and Loading
- PCA on the Wine Dataset
- PCA on the iris Dataset
- Interpreting Results of Factor Analysis
| Link to Video will be provided here. |
Day 8 – Cluster Analysis |
- Review of K-Nearest Neighbors Classifier
- Types of Clustering (Hierarchical and Agglomerative Clustering)
- K-Means Clustering
- The K-Means Algorithm
- Trade-off between K-means and Hierarchical
- The Dendrogram
| Link to Video will be provided here. |
Day 9 – Neural Networks |
- Background of the Artificial Neural Networks.
- Review of Calculus(differentiation and partial derivatives).
- The Sigmoid Neuron.
- Concept of weights and biases.
- Network activation and activation function.
- Hidden layers. The Perceptron.
- Multilayer Perceptron.
- Network training.
- Backpropagation and Gradient decent.
- Network training algorithms.
- Demo: Building a Neural Network Model for Classification for the MNIST fashion Dataset
| Link to Video will be provided here. |
Day 10 – Introduction to TensorFlow |
- What is TensorFlow? What is a Tensor?
- Introduction and Setup of Tensorflow and keras with Anaconder Navigator
- Import and view the MNIST Fashion Dataset
- Examine the Image Data
- Preprocessing of Data
- Setup Neural Network Layers
- Compile the Model
- Train the Model
- Make Predictions
- Evaluate Model Results(1)
- Evaluate Model Results(2)
- Prediction on Single Image
| Link to Video will be provided here. |
After this, we would start another Data Science in 10 Days Series but with different set’s of topics and probably a bit more challenging!