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  1. Home/
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  3. Python for Data Science

Python for Data Science

Python for Data Science

Unlock the power of Python for data analysis, visualisation, and machine learning with this comprehensive Python for Data Science course. Designed for learners who already have a solid understanding of Python programming, this hands-on course focuses on the essential libraries, tools, and techniques used by data scientists to collect, clean, analyse, visualise, and interpret data.

Throughout the course, you will learn how to work with NumPy, Pandas, Matplotlib, and Seaborn to manipulate datasets, perform statistical analysis, create insightful visualisations, and prepare data for machine learning. You will also explore the fundamentals of machine learning using Scikit-learn, including data preprocessing, model training, evaluation, and prediction.

By the end of the course, you will be able to analyse real-world datasets, build data-driven solutions, create interactive visualisations, and develop machine learning models that solve practical business problems. This course is ideal for software developers, data analysts, aspiring data scientists, engineers, and anyone looking to transition into the field of data science using Python.

Course Prerequisites:

Basics of Python programming

Course Snapshot

£30.00 / Hour

Level: AdvancedDuration: 20 HoursMode: Online / Virtual

What you will learn?

1. Introduction to Data Science with Python

  • What is Data Science?
  • Data Science Workflow
  • Setting up the Environment
  • Jupyter Notebook
  • Google Colab
  • Python Virtual Environments
  • Installing Data Science Libraries
  • Working with CSV, Excel and JSON Files

2. NumPy Fundamentals

  • Introduction to NumPy
  • Creating Arrays
  • Array Operations
  • Array Indexing
  • Array Slicing
  • Boolean Indexing
  • Broadcasting
  • Mathematical Operations
  • Universal Functions (ufuncs)
  • Array Reshaping
  • Aggregation Functions
  • Performance Comparison: Lists vs NumPy Arrays

3. Data Analysis with Pandas

  • Introduction to Pandas
  • Series
  • DataFrames
  • Importing Data
  • Exporting Data
  • Selecting Rows and Columns
  • Filtering Data
  • Sorting Data
  • Handling Missing Values
  • Removing Duplicates
  • Renaming Columns
  • Creating New Columns
  • String Operations
  • Date and Time Operations
  • GroupBy
  • Aggregations
  • Merge
  • Join
  • Concatenate
  • Pivot Tables
  • Cross Tabulation

4. Data Cleaning & Data Preparation

  • Data Quality Issues
  • Missing Data
  • Outlier Detection
  • Duplicate Records
  • Feature Engineering
  • Data Transformation
  • Normalisation
  • Standardisation
  • Encoding Categorical Variables
  • Binning
  • Data Validation

5. Data Visualisation

  • Introduction to Matplotlib
  • Line Charts
  • Bar Charts
  • Pie Charts
  • Scatter Plots
  • Histograms
  • Box Plots
  • Heatmaps
  • Pair Plots
  • Distribution Plots
  • Seaborn Basics
  • Statistical Charts
  • Customising Charts
  • Multi-plot Visualisations

6. Exploratory Data Analysis (EDA)

  • Understanding the Dataset
  • Summary Statistics
  • Correlation Analysis
  • Trend Analysis
  • Distribution Analysis
  • Detecting Patterns
  • Identifying Relationships
  • Finding Anomalies
  • Creating EDA Reports

7. Statistics for Data Science

  • Mean
  • Median
  • Mode
  • Variance
  • Standard Deviation
  • Percentiles
  • Quartiles
  • Probability Basics
  • Normal Distribution
  • Z-Score
  • Correlation
  • Covariance

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