Dimitris Kyrtopoulos | dk

IBM Data Analysis with Python

IBM Data Analysis with Python Dimitris Kyrtopoulos



About this Course

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!

Topics covered:

1) Importing Datasets
2) Cleaning the Data
3) Data frame manipulation
4) Summarizing the Data
5) Building machine learning Regression models
6) Building data pipelines

Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts:

Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.

What you will learn

  • Describe Python data acquisition and analysis techniques.

  • Analyze Python data using a dataset.

  • Identify three Python libraries and describe their uses.

  • Read data using Python’s Pandas package.

Skills you will gain

Predictive Modelling
Python Programming
Data Analysis
Data Visualization (DataViz)
Model Selection


Week 1: Importing Datasets
In this module, you will learn how to understand data and learn about how to use the libraries in Python to help you import data from multiple sources. You will then learn how to perform some basic tasks to start exploring and analyzing the imported data set.

Week 2: Data Wrangling
In this module, you will learn how to perform some fundamental data wrangling tasks that, together, form the pre-processing phase of data analysis. These tasks include handling missing values in data, formatting data to standardize it and make it consistent, normalizing data, grouping data values into bins, and converting categorical variables into numerical quantitative variables.

Week 3: Exploratory Data Analysis
In this module, you will learn what is meant by exploratory data analysis, and you will learn how to perform computations on the data to calculate basic descriptive statistical information, such as mean, median, mode, and quartile values, and use that information to better understand the distribution of the data. You will learn about putting your data into groups to help you visualize the data better, you will learn how to use the Pearson correlation method to compare two continuous numerical variables, and you will learn how to use the Chi-square test to find the association between two categorical variables and how to interpret them.

Week 4: Model Development
In this module, you will learn how to define the explanatory variable and the response variable and understand the differences between the simple linear regression and multiple linear regression models. You will learn how to evaluate a model using visualization and learn about polynomial regression and pipelines. You will also learn how to interpret and use the R-squared and the mean square error measures to perform in-sample evaluations to numerically evaluate our model. And lastly, you will learn about prediction and decision making when determining if our model is correct.

Week 5: Model Evaluation
In this module, you will learn about the importance of model evaluation and discuss different data model refinement techniques. You will learn about model selection and how to identify overfitting and underfitting in a predictive model. You will also learn about using Ridge Regression to regularize and reduce standard errors to prevent overfitting a regression model and how to use the Grid Search method to tune the hyperparameters of an estimator.

Week 6: Final Assignment
Congratulations! You have now completed all the modules for this course. In this last module, you will complete the final assignment that will be graded by your peers. In this final assignment, you will assume the role of a Data Analyst working at a real estate investment trust organization who wants to start investing in residential real estate. You will be given a dataset containing detailed information about house prices in the region based on a number of property features, and it will be your job to analyze and predict the market price of houses given that information.