{"id":347,"date":"2019-11-04T21:56:36","date_gmt":"2019-11-04T21:56:36","guid":{"rendered":"https:\/\/www.kindsonthegenius.com\/python\/?page_id=347"},"modified":"2026-07-03T21:35:13","modified_gmt":"2026-07-03T21:35:13","slug":"data-science-in-10-days-simplified-for-non-techies","status":"publish","type":"page","link":"https:\/\/kindsonthegenius.com\/python\/data-science-in-10-days-simplified-for-non-techies\/","title":{"rendered":"Data Science in 10 Days (Simplified for Non-Techies)"},"content":{"rendered":"

Welcome to Data Science in 10 Days!. Hopefully, you have completed Python in 10 Days<\/a> as this follows from Python in 10 Days<\/a>.<\/p>\n

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.<\/p>\n

Feel free to mention if you have any challenges. Ensure to subscribe so you get notified when a new lesson is released.<\/p>\n

Complete Python Tutorials<\/a><\/p>\n\n\n\n\n\n
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Day 1 \u2013 Introduction\/Review of Python\/R, Jupyter Notebook, Anaconda Navigator<\/strong><\/h5>\n<\/td>\n<\/tr>\n<\/thead>\n
\n
    \n
  • Concept of Data Science and Types of Data<\/li>\n
  • Review of Python and Jupyter Notebook<\/li>\n
  • Adding Modules using Anaconda Navigator<\/li>\n
  • Setting up and Using R Studio<\/li>\n
  • Rewriting Arithmetic Expressions<\/li>\n<\/ul>\n<\/td>\n
Link to Video will be provided here.<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n
\n
Day 2 \u2013 Data Acquisition and Preparation (Various Sources of Free Datasets)<\/strong><\/h5>\n<\/td>\n<\/tr>\n<\/thead>\n
\n
    \n
  • Getting Datasets from R<\/li>\n
  • Exporting from R<\/li>\n
  • Importing to Python (.txt, .xls, .xlsx,.data and others)<\/li>\n
  • Getting Dataset From Machine Learning Repository<\/li>\n
  • Getting Dataset from MS Azure ML Studio<\/li>\n
  • Generating a Custom Dataset using Python Functions<\/li>\n
  • Python Dictionary, Lists, Tuples and Sets<\/li>\n
  • Numpy Arrays, Matrices and Pandas DataFrame<\/li>\n
  • Creating a Pandas DataFrame<\/li>\n<\/ul>\n<\/td>\n
Link to Video will be provided here.<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n
\n
Day 3 \u2013 Plotting and Data Visualization<\/strong><\/h5>\n<\/td>\n<\/tr>\n<\/thead>\n
\n
    \n
  • Introduction and Basics of Plotting<\/li>\n
  • Formatting Your Plot<\/li>\n
  • Formatting Your Plot Using shorthand<\/li>\n
  • Matplotlib.Pyplot Functions<\/li>\n
  • Plotting the Heart Curve and the Figure 8\u00a0Shape<\/li>\n
  • Working with Subplots<\/li>\n
  • Creating as Scatterplot<\/li>\n
  • Creating a Histogram<\/li>\n
  • Tutorial 10 – Creating a Bar Chart<\/li>\n
  • Using a Heatmap<\/li>\n
  • Creating a Pairplot (Scatter Matrix)<\/li>\n<\/ul>\n<\/td>\n
Link to Video will be provided here.<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n
\n
Day 4 \u2013 Regression Analysis I, Regression Analysis II<\/strong><\/h5>\n<\/td>\n<\/tr>\n<\/thead>\n
\n
    \n
  • Types of Regression<\/li>\n
  • Deducing the Equation of a Regression Line<\/li>\n
  • Linear Regression Demo in Python<\/li>\n
  • Polynomial Regression<\/li>\n
  • Multiple Regression<\/li>\n
  • Making Inference and Prediction<\/li>\n
  • Discreet and continuous variable<\/li>\n
  • Quantitative and Qualitative Data<\/li>\n<\/ul>\n<\/td>\n
Link to Video will be provided here.<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n
\n
Day 5 \u2013\u00a0 Logistic Regression and Classification<\/strong><\/h5>\n<\/td>\n<\/tr>\n<\/thead>\n
\n
    \n
  • Basics of Logistic Regression<\/li>\n
  • The Logistic Function<\/li>\n
  • Odds and Odds ratio<\/li>\n
  • Overview of Some Statistical Concepts<\/li>\n
  • Performing Classification on Discreet data<\/li>\n
  • A little about Probability Theory<\/li>\n<\/ul>\n<\/td>\n
Link to Video will be provided here.<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n
\n
Day 6 \u2013 <\/strong>Decision Trees, Building Classifiers<\/strong><\/h5>\n<\/td>\n<\/tr>\n<\/thead>\n
\n
    \n
  • Basics of Decision Trees<\/li>\n
  • Decision Trees for Classification and Regression<\/li>\n
  • Concept of Pruning<\/li>\n
  • Demo: Building a Decision Tree Classifier<\/li>\n
  • The Bayes’ Classifier<\/li>\n
  • K-Nearest Neighbor Classifier<\/li>\n<\/ul>\n<\/td>\n
Link to Video will be provided here.<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n
\n
Day 7 \u2013 Factor Analysis and Principal Components Analysis<\/strong><\/h5>\n<\/td>\n<\/tr>\n<\/thead>\n
\n
    \n
  • Introduction to PCA<\/li>\n
  • Introduction to Factor Analysis<\/li>\n
  • Test for Sampling Adequacy<\/li>\n
  • The KMO Statistics<\/li>\n
  • Concept of Scores and Loading<\/li>\n
  • PCA on the Wine Dataset<\/li>\n
  • PCA on the iris Dataset<\/li>\n
  • Interpreting Results of Factor Analysis<\/li>\n<\/ul>\n<\/td>\n
Link to Video will be provided here.<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n
\n
Day 8 \u2013 Cluster Analysis<\/strong><\/h5>\n<\/td>\n<\/tr>\n<\/thead>\n
\n
    \n
  • Review of K-Nearest Neighbors Classifier<\/li>\n
  • Types of Clustering (Hierarchical and Agglomerative Clustering)<\/li>\n
  • K-Means Clustering<\/li>\n
  • The K-Means Algorithm<\/li>\n
  • Trade-off between K-means and Hierarchical<\/li>\n
  • The Dendrogram<\/li>\n<\/ul>\n<\/td>\n
Link to Video will be provided here.<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n
\n
Day 9 \u2013 Neural Networks<\/strong><\/h5>\n<\/td>\n<\/tr>\n<\/thead>\n
\n
    \n
  • Background of the Artificial Neural Networks.<\/li>\n
  • Review of Calculus(differentiation and partial derivatives).<\/li>\n
  • The Sigmoid Neuron.<\/li>\n
  • Concept of weights and biases.<\/li>\n
  • Network activation and activation function.<\/li>\n
  • Hidden layers. The Perceptron.<\/li>\n
  • Multilayer Perceptron.<\/li>\n
  • Network training.<\/li>\n
  • Backpropagation and Gradient decent.<\/li>\n
  • Network training algorithms.<\/li>\n
  • Demo: Building a Neural Network Model for Classification for the MNIST fashion Dataset<\/li>\n<\/ul>\n<\/td>\n
Link to Video will be provided here.<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n
\n
Day 10 \u2013 <\/strong>Introduction to TensorFlow<\/strong><\/h5>\n<\/td>\n<\/tr>\n<\/thead>\n
\n
    \n
  • What is TensorFlow? What is a Tensor?<\/li>\n
  • Introduction and Setup of Tensorflow and keras with Anaconder Navigator<\/li>\n
  • Import and view the MNIST Fashion Dataset<\/li>\n
  • Examine the Image Data<\/li>\n
  • Preprocessing of Data<\/li>\n
  • Setup Neural Network Layers<\/li>\n
  • Compile the Model<\/li>\n
  • Train the Model<\/li>\n
  • Make Predictions<\/li>\n
  • Evaluate Model Results(1)<\/li>\n
  • Evaluate Model Results(2)<\/li>\n
  • Prediction on Single Image<\/li>\n<\/ul>\n<\/td>\n
Link to Video will be provided here.<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

After this, we would start another Data Science in 10 Days Series but with different set’s of topics\u00a0 and probably a bit more challenging!<\/p>\n

 <\/p>\n

<\/p>\n

\n

Continue to data science:<\/strong> Follow the Data Science Class Series<\/a> on our data science tutorials<\/a> subsite.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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