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Python® for Data Science for Beginners

This course is designed for individuals with little to no prior programming experience who are interested in learning Python for data science applications.Grasp Python Fundamentals: You’ll acquire the foundational knowledge of Python programming, including variables, data types, operators, control flow statements, functions, and libraries.Learn Data Structures: The course will introduce you to essential data structures in Python like lists, tuples, dictionaries, and sets, used to organize and manipulate data for analysis.Work with Libraries: A core focus will be on prominent Python libraries like NumPy for numerical computing, Pandas for data analysis and manipulation, and Matplotlib/Seaborn for data visualization. You’ll learn how to import, use functions, and explore core functionalities of these libraries.Perform Data Cleaning and Exploration: The course will guide you through processes like data cleaning (handling missing values, inconsistencies) and data exploration (calculating summary statistics, identifying patterns and trends) to prepare your data for further analysis.Introduction to Data Visualization: You’ll be introduced to creating basic data visualizations like histograms, scatter plots, and box plots using libraries like Matplotlib or Seaborn to represent data insights visually.
Python® for Data Science for Beginners
Test Prep
30+ LiveLab | 15+ Video tutorials | 24+ Minutes

Why choose TOPTALENT?


Lessons 1:

  • About This Course
  • False Assumptions
  • Icons Used in This Course
  • Where to Go from Here

Lessons 2:
Discovering the Match between Data Science and Python

  • Defining the Sexiest Job of the 21st Century
  • Creating the Data Science Pipeline
  • Understanding Python’s Role in Data Science
  • Learning to Use Python Fast

Lessons 3:
Introducing Python’s Capabilities and Wonders

  • Why Python?
  • Working with Python
  • Performing Rapid Prototyping and Experimentation
  • Considering Speed of Execution
  • Visualizing Power
  • Using the Python Ecosystem for Data Science

Lessons 4:
Setting Up Python for Data Science

  • Considering the Off-the-Shelf Cross-Platform Scientific Distributions
  • Installing Anaconda on Windows
  • Installing Anaconda on Linux
  • Installing Anaconda on Mac OS X
  • Downloading the Datasets and Example Code

Lessons 5:
Working with Google Colab

  • Defining Google Colab
  • Getting a Google Account
  • Working with Notebooks
  • Performing Common Tasks
  • Using Hardware Acceleration
  • Executing the Code
  • Viewing Your Notebook
  • Sharing Your Notebook
  • Getting Help

Lessons 6:
Understanding the Tools

  • Using the Jupyter Console
  • Using Jupyter Notebook
  • Performing Multimedia and Graphic Integration

Lessons 7:
Working with Real Data

  • Uploading, Streaming, and Sampling Data
  • Accessing Data in Structured Flat-File Form
  • Sending Data in Unstructured File Form
  • Managing Data from Relational Databases
  • Interacting with Data from NoSQL Databases
  • Accessing Data from the Web

Lessons 8:
Conditioning Your Data

  • Juggling between NumPy and pandas
  • Validating Your Data
  • Manipulating Categorical Variables
  • Dealing with Dates in Your Data
  • Dealing with Missing Data
  • Slicing and Dicing: Filtering and Selecting Data
  • Concatenating and Transforming
  • Aggregating Data at Any Level

Lessons 9:
Shaping Data

  • Working with HTML Pages
  • Working with Raw Text
  • Using the Bag of Words Model and Beyond
  • Working with Graph Data

Lessons 10:
Putting What You Know in Action

  • Contextualizing Problems and Data
  • Considering the Art of Feature Creation
  • Performing Operations on Arrays

Lessons 11:
Getting a Crash Course in MatPlotLib

  • Starting with a Graph
  • Setting the Axis, Ticks, Grids
  • Defining the Line Appearance
  • Using Labels, Annotations, and Legends

Lessons 12:
Visualizing the Data

  • Choosing the Right Graph
  • Creating Advanced Scatterplots
  • Plotting Time Series
  • Plotting Geographical Data
  • Visualizing Graphs

Lessons 13:
Stretching Python’s Capabilities

  • Playing with Scikit-learn
  • Performing the Hashing Trick
  • Considering Timing and Performance
  • Running in Parallel on Multiple Cores

Lessons 14:
Exploring Data Analysis

  • The EDA Approach
  • Defining Descriptive Statistics for Numeric Data
  • Counting for Categorical Data
  • Creating Applied Visualization for EDA
  • Understanding Correlation
  • Modifying Data Distributions

Lessons 15:
Reducing Dimensionality

  • Understanding SVD
  • Performing Factor Analysis and PCA
  • Understanding Some Applications

Lessons 16:

  • Clustering with K-means
  • Performing Hierarchical Clustering
  • Discovering New Groups with DBScan

Lessons 17:
Detecting Outliers in Data

  • Considering Outlier Detection
  • Examining a Simple Univariate Method
  • Developing a Multivariate Approach

Lessons 18:
Exploring Four Simple and Effective Algorithms

  • Guessing the Number: Linear Regression
  • Moving to Logistic Regression
  • Making Things as Simple as Naïve Bayes
  • Learning Lazily with Nearest Neighbors

Lessons 19:
Performing Cross-Validation, Selection, and Optimization

  • Pondering the Problem of Fitting a Model
  • Cross-Validating
  • Selecting Variables Like a Pro
  • Pumping Up Your Hyperparameters

Lessons 20:
Increasing Complexity with Linear and Nonlinear Tricks

  • Using Nonlinear Transformations
  • Regularizing Linear Models
  • Fighting with Big Data Chunk by Chunk
  • Understanding Support Vector Machines
  • Playing with Neural Networks

Lessons 21:
Understanding the Power of the Many

  • Starting with a Plain Decision Tree
  • Making Machine Learning Accessible
  • Boosting Predictions

Lessons 22:
Ten Essential Data Resources

  • Discovering the News with Subreddit
  • Getting a Good Start with KDnuggets
  • Locating Free Learning Resources with Quora
  • Gaining Insights with Oracle’s Data Science Blog
  • Accessing the Huge List of Resources on Data Science Central
  • Learning New Tricks from the Aspirational Data Scientist
  • Obtaining the Most Authoritative Sources at Udacity
  • Receiving Help with Advanced Topics at Conductrics
  • Obtaining the Facts of Open Source Data Science from Masters
  • Zeroing In on Developer Resources with Jonathan Bower

Lessons 23:
Ten Data Challenges You Should Take

  • Meeting the Data Science London + Scikit-learn Challenge
  • Predicting Survival on the Titanic
  • Finding a Kaggle Competition that Suits Your Needs
  • Honing Your Overfit Strategies
  • Trudging Through the MovieLens Dataset
  • Getting Rid of Spam E-mails
  • Working with Handwritten Information
  • Working with Pictures
  • Analyzing Reviews
  • Interacting with a Huge Graph