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Python With Data Science

4.6 out of 5 rating Last updated 14/11/2024   English

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Duration

2 Days

12 CPD hours

Overview

NumPy, pandas, Matplotlib, scikit-learn
Python REPLs
Jupyter Notebooks
Data analytics life-cycle phases
Data repairing and normalizing
Data aggregation and grouping
Data visualization
Data science algorithms for supervised and unsupervised machine learning

Description

Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases.

Python for Data Science
  • Using Modules
  • Listing Methods in a Module
  • Creating Your Own Modules
  • List Comprehension
  • Dictionary Comprehension
  • String Comprehension
  • Python 2 vs Python 3
  • Sets (Python 3+)
  • Python Idioms
  • Python Data Science Ecosystem
  • NumPy
  • NumPy Arrays
  • NumPy Idioms
  • pandas
  • Data Wrangling with pandas' DataFrame
  • SciPy
  • Scikit-learn
  • SciPy or scikit-learn
  • Matplotlib
  • Python vs R
  • Python on Apache Spark
  • Python Dev Tools and REPLs
  • Anaconda
  • IPython
  • Visual Studio Code
  • Jupyter
  • Jupyter Basic Commands
  • Summary
Applied Data Science
  • What is Data Science
  • Data Science Ecosystem
  • Data Mining vs. Data Science
  • Business Analytics vs. Data Science
  • Data Science, Machine Learning, AI
  • Who is a Data Scientist
  • Data Science Skill Sets Venn Diagram
  • Data Scientists at Work
  • Examples of Data Science Projects
  • An Example of a Data Product
  • Applied Data Science at Google
  • Data Science Gotchas
  • Summary
Data Analytics Life-cycle Phases
  • Big Data Analytics Pipeline
  • Data Discovery Phase
  • Data Harvesting Phase
  • Data Priming Phase
  • Data Logistics and Data Governance
  • Exploratory Data Analysis
  • Model Planning Phase
  • Model Building Phase
  • Communicating the Results
  • Production Roll-out
  • Summary
Repairing and Normalizing Data
  • Repairing and Normalizing Data
  • Dealing with the Missing Data
  • Sample Data Set
  • Getting Info on Null Data
  • Dropping a Column
  • Interpolating Missing Data in pandas
  • Replacing the Missing Values with the Mean Value
  • Scaling (Normalizing) the Data
  • Data Preprocessing with scikit-learn
  • Scaling with the scale() Function
  • The MinMaxScaler Object
  • Summary
Descriptive Statistics Computing Features in Python
  • Descriptive Statistics
  • Non-uniformity of a Probability Distribution
  • Using NumPy for Calculating Descriptive Statistics Measures
  • Finding Min and Max in NumPy
  • Using pandas for Calculating Descriptive Statistics Measures
  • Correlation
  • Regression and Correlation
  • Covariance
  • Getting Pairwise Correlation and Covariance Measures
  • Finding Min and Max in pandas DataFrame
  • Summary
Data Aggregation and Grouping
  • Data Aggregation and Grouping
  • Sample Data Set
  • The pandas.core.groupby.SeriesGroupBy Object
  • Grouping by Two or More Columns
  • Emulating the SQL's WHERE Clause
  • The Pivot Tables
  • Cross-Tabulation
  • Summary
Data Visualization with matplotlib
  • Data Visualization
    What is matplotlib
    Getting Started with matplotlib
    The Plotting Window
    The Figure Options
    The matplotlib.pyplot.plot() Function
    The matplotlib.pyplot.bar() Function
    The matplotlib.pyplot.pie () Function
    Subplots
    Using the matplotlib.gridspec.GridSpec Object
    The matplotlib.pyplot.subplot() Function
    Hands-on Exercise
    Figures
    Saving Figures to File
    Visualization with pandas
    Working with matplotlib in Jupyter Notebooks
    Summary
Data Science and ML Algorithms in scikit-learn
  • Data Science, Machine Learning, AI
  • Types of Machine Learning
  • Terminology: Features and Observations
  • Continuous and Categorical Features (Variables)
  • Terminology: Axis
  • The scikit-learn Package
  • scikit-learn Estimators
  • Models, Estimators, and Predictors
  • Common Distance Metrics
  • The Euclidean Metric
  • The LIBSVM format
  • Scaling of the Features
  • The Curse of Dimensionality
  • Supervised vs Unsupervised Machine Learning
  • Supervised Machine Learning Algorithms
  • Unsupervised Machine Learning Algorithms
  • Choose the Right Algorithm
  • Life-cycles of Machine Learning Development
  • Data Split for Training and Test Data Sets
  • Data Splitting in scikit-learn
  • Hands-on Exercise
  • Classification Examples
  • Classifying with k-Nearest Neighbors (SL)
  • k-Nearest Neighbors Algorithm
  • k-Nearest Neighbors Algorithm
  • The Error Rate
  • Hands-on Exercise
  • Dimensionality Reduction
  • The Advantages of Dimensionality Reduction
  • Principal component analysis (PCA)
  • Hands-on Exercise
  • Data Blending
  • Decision Trees (SL)
  • Decision Tree Terminology
  • Decision Tree Classification in Context of Information Theory
  • Information Entropy Defined
  • The Shannon Entropy Formula
  • The Simplified Decision Tree Algorithm
  • Using Decision Trees
  • Random Forests
  • SVM
  • Naive Bayes Classifier (SL)
  • Naive Bayesian Probabilistic Model in a Nutshell
  • Bayes Formula
  • Classification of Documents with Naive Bayes
  • Unsupervised Learning Type: Clustering
  • Clustering Examples
  • k-Means Clustering (UL)
  • k-Means Clustering in a Nutshell
  • k-Means Characteristics
  • Regression Analysis
  • Simple Linear Regression Model
  • Linear vs Non-Linear Regression
  • Linear Regression Illustration
  • Major Underlying Assumptions for Regression Analysis
  • Least-Squares Method (LSM)
  • Locally Weighted Linear Regression
  • Regression Models in Excel
  • Multiple Regression Analysis
  • Logistic Regression
  • Regression vs Classification
  • Time-Series Analysis
  • Decomposing Time-Series
  • Summary
Lab Exercises
  • Lab 1 - Learning the Lab Environment
  • Lab 2 - Using Jupyter Notebook
  • Lab 3 - Repairing and Normalizing Data
  • Lab 4 - Computing Descriptive Statistics
  • Lab 5 - Data Grouping and Aggregation
  • Lab 6 - Data Visualization with matplotlib
  • Lab 7 - Data Splitting
  • Lab 8 - k-Nearest Neighbors Algorithm
  • Lab 9 - The k-means Algorithm
  • Lab 10 - The Random Forest Algorithm
Additional course details:

Nexus Humans Python With Data Science training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward.

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the Python With Data Science course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

FAQ for the Python With Data Science Course

Available Delivery Options for the Python With Data Science training.
  • Live Instructor Led Classroom Online (Live Online)
  • Traditional Instructor Led Classroom (TILT/ILT)
  • Delivery at your offices in London or anywhere in the UK
  • Private dedicated course as works for your staff.
How many CPD hours does the Python With Data Science training provide?

The 2 day. Python With Data Science training course give you up to 12 CPD hours/structured learning hours. If you need a letter or certificate in a particular format for your association, organisation or professional body please just ask.

What is the correct audience for the Python With Data Science training?

Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming
Also familiar with Python

Do you provide training for the Python With Data Science.

Yes we provide corporate training, dedicated training and closed classes for the Python With Data Science. This can take place anywhere in UK including, England, Scotland, Cymru (Wales) or Northern Ireland or live online allowing you to have your teams from across UK or further afield to attend a single training event saving travel and delivery expenses.

What is the duration of the Python With Data Science program.

The Python With Data Science training takes place over 2 day(s), with each day lasting approximately 8 hours including small and lunch breaks to ensure that the delegates get the most out of the day.

Why are Nexus Human the best provider for the Python With Data Science?
Nexus Human are recognised as one of the best training companies as they and their trainers have won and hold many awards and titles including having previously won the Small Firms Best Trainer award, national training partner of the year for UK on multiple occasions, having trainers in the global top 30 instructor awards in 2012, 2019 and 2021. Nexus Human has also been nominated for the Tech Excellence awards multiple times. Learning Performance institute (LPI) external training provider sponsor 2024.
Is there a discount code for the Python With Data Science training.

Yes, the discount code PENPAL5 is currently available for the Python With Data Science training. Other discount codes may also be available but only one discount code or special offer can be used for each booking. This discount code is available for companies and individuals.

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Training Insurance Included!

When you organise training, we understand that there is a risk that some people may fall ill, become unavailable. To mitigate the risk we include training insurance for each delegate enrolled on our public schedule, they are welcome to sit on the same Public class within 6 months at no charge, if the case arises.

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