A 50-Day Guide to Becoming a Data Scientist with Python, SQL, and Machine Learning

Welcome to the Data Science course! Over the next 50 days, you will learn a wide range of topics related to Python programming, data science, and machine learning. These topics will be covered in a variety of posts, so be sure to bookmark this page and follow me here and on GitHub for updates.

Throughout the course, you will have the opportunity to work with real-world data sets and apply the concepts you have learned to solve practical problems. You will also find exercises in each post that you can practice to further solidify your understanding of the material. All materials and exercises will be available on the GitHub repository linked below.

By the end of the course, you will have a strong foundation in data science and be well-prepared to pursue further study or a career in the field. So let's get started!

1. Python

   1. Python basics

      1. Input/Output

         • Printing to the console

         • Getting input from the user

      2. Operators

         • Arithmetic operators (e.g. +, -, *, /)

         • Comparison operators (e.g. ==, !=, >, <)

         • Logical operators (e.g. and, or, not)

      3. Operations

         • Working with variables

         • Data types (e.g. int, float, str)

         • Type conversion

         • Basic string manipulation (e.g. indexing, slicing, concatenation)

   2. Python data structures

      1. list

         • Creating and accessing lists

         • Modifying lists (e.g. adding, removing, and sorting elements)

         • Looping through lists

      2. tuple

         • Creating and accessing tuples

         • Modifying tuples (e.g. adding and removing elements)

         • Looping through tuples

      3. set

         • Creating and accessing sets

         • Modifying sets (e.g. adding, removing, and intersecting elements)

         • Looping through sets

      4. dictionary

         • Creating and accessing dictionaries

         • Modifying dictionaries (e.g. adding, removing, and updating key-value pairs)

         • Looping through dictionaries

   3. Python fundamentals

      1. loops

         • For loops

         • While loops

         • Break and continue statements

      2. functions

         • Defining and calling functions

         • Parameters and arguments

         • Return values

      3. object and classes

         • Defining classes and objects

         • Constructors and destructors

         • Inheritance

         • Method overloading and overriding

   4. Pandas

      • Introduction to Pandas library

      • Loading and saving data with Pandas

      • Working with DataFrames and Series

      • Manipulating and cleaning data with Pandas

   5. Numpy

      • Introduction to Numpy library

      • Creating and accessing arrays

      • Array operations (e.g. reshaping, slicing, and element-wise operations)

      • Mathematical and statistical functions

   6. Matplotlib

      • Introduction to Matplotlib library

      • Creating basic plots (e.g. line, scatter, and bar plots)

      • Customizing plots (e.g. labels, titles, and legends)

      • Saving and showing plots

2. SQL

   • Introduction to Structured Query Language (SQL)

   • Creating and modifying databases and tables

   • Selecting, filtering, and sorting data

   • Grouping and aggregating data

   • Joining tables

   • Subqueries and views

3. Maths Refresher

   1. Statistics

      • Mean, median, mode

      • Range, variance, standard deviation

      • Percentiles and quartiles

      • Z-scores

   2. Probability

      • Basic probability concepts (e.g. events, sample space, and probability)

      • Conditional probability and independence

   3. Linear algebra

      • Vectors and matrices

      • Matrix operations (e.g. addition, multiplication, and transposition)

   4. Calculus

      • Limits and continuity

      • Derivatives

      • Integrals

      • Fundamental theorem of calculus

4. Python for data science

   1. Jupyter notebook and google collab walkthrough

      • Introduction to Jupyter notebooks and Google Colab

      • Creating and running cells

      • Importing and exporting notebooks

   2. Python data science libraries

      • Introduction to popular data science libraries (e.g. Scikit-learn, TensorFlow, and Keras)

      • Installing and importing libraries

   3. Exploratory data analysis

      1. Visualization

         • Introduction to Matplotlib and Seaborn

         • Plotting distributions, scatterplots, and boxplots

         • Customizing plots

      2. Summary statistics

         • Calculating basic statistics (e.g. mean, median, and standard deviation)

         • Generating descriptive statistics with Pandas

      3. Correlation analysis

         • Calculating and interpreting correlations

         • Visualizing correlations with scatterplots

      4. Data cleaning

         • Handling missing values

         • Removing outliers

         • Normalizing and standardizing data

      5. Dimension reduction

         • Introduction to dimension reduction techniques (e.g. PCA and t-SNE)

         • Implementing and interpreting dimension reduction in Python

      6. Anomaly detection

         • Introduction to anomaly detection techniques (e.g. isolation forests and local outlier factor)

         • Implementing and interpreting anomaly detection in Python

      7. Feature engineering

         • Introduction to feature engineering

         • Creating new features from existing data

         • Selecting relevant features for model building

5. Machine learning

   1. Introduction

      • Definition and types of machine learning

      • Differences between supervised, unsupervised, and reinforcement learning

   2. Supervised learning

      • Regression and classification algorithms

      • Evaluation metrics for regression and classification models (e.g. mean squared error and accuracy)

   3. Classification

      • K-nearest neighbors (KNN)

      • Logistic regression

      • Support vector machines (SVM)

   4. Decision trees

      • Introduction to decision trees

      • Implementing decision trees in Python

      • Visualizing decision trees

   5. Time series prediction

      • Introduction to time series data

      • Moving average and exponential smoothing models

      • Autoregressive integrated moving average (ARIMA) model

   6. Unsupervised learning

      • Clustering algorithms (e.g. k-means and hierarchical clustering)

      • Evaluation metrics for clustering (e.g. silhouette score and calinski-harabasz index)

   7. Some projects (5-8)

      • Suggested projects to apply machine learning concepts (e.g. building a spam detector or a customer segmentation model)

6. Tableau

   • Connecting to and importing data

   • Working with data in Tableau

   • Creating and customizing visualizations

   • Dashboarding and storytelling with Tableau

   • Advanced techniques (e.g. calculated fields, parameters, and table calculations)

   • Exporting and publishing dashboards

We hope that you will enjoy learning about data science with me! By completing this course, you should now have a strong foundation in Python programming, SQL, maths refresher, data science with Python, machine learning, and Tableau. You should be well-prepared to pursue further study or a career in the field, and we encourage you to continue learning and staying up-to-date on new developments in the world of data science.

We would like to thank you for joining me on this journey and hope that you will continue to follow us for future updates and learning opportunities. Don't forget to check out the GitHub repository linked below for all materials and exercises, and we look forward to seeing what you will accomplish with your new skills!