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Applied Data Science with Python

Applied Data Science with Python

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Recorded content
Of Total 10 Hrs.
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Duration
2 days (13 hours)
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LIVE sessions
4 Workshops
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Hands-On Learning
With Practice Modules
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Certificate
With License

Overview

This Applied Data Science with Python training course teaches attendees the fundamentals of using Python to program tasks in data science, data engineering, business analytics, and data visualization.

Objective

  • Use Jupyter Notebooks
  • Understand Python
  • Work with NumPy and pandas
  • Repair and normalize data
  • Work with data visualization in Python
  • Perform data splitting
  • Work with the Random Forest algorithm
  • Understand the k-Means algorithm
Join Next Batch Starting on September 10th

Outline

  • • Python Data Science-Centric Libraries
  • • NumPy
  • • SciPy
  • • Pandas
  • • Creating a pandas DataFrame
  • • Fetching and Sorting Data
  • • Scikit-learn
  • • Matplotlib
  • • Seaborn
  • • Python Dev Tools and REPLs
  • • IPython
  • • Jupyter
  • • Anaconda
  • • What is Data Science?
  • • Data Science, Machine Learning, AI?
  • • The Data-Related Roles
  • • The Data Science Ecosystem
  • • Tools of the Trade
  • • Who is a Data Scientist?
  • • Data Scientists at Work
  • • Examples of Data Science Projects
  • • An Example of a Data Product
  • • Applied Data Science at Google
  • • Data Science Gotchas

  • • Typical Data Processing Pipeline
  • • Data Discovery Phase
  • • Data Harvesting Phase
  • • Data Priming Phase
  • • Exploratory Data Analysis
  • • Model Planning Phase
  • • Model Building Phase
  • • Communicating the Results
  • • Production Roll-out
  • • Data Logistics and Data Governance
  • • Data Processing Workflow Engines
  • • Apache Airflow
  • • Data Lineage and Provenance
  • • Apache NiFi
  • • 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
  • • 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
  • • Data Visualization
  • • Data Visualization in Python
  • • Matplotlib
  • • Getting Started with matplotlib
  • • Subplots
  • • Using the matplotlib.gridspec.GridSpec Object
  • • The matplotlib.pyplot.subplot() Function
  • • Figures
  • • Saving Figures to a File
  • • Seaborn
  • • Getting Started with seaborn
  • • Histograms and KDE
  • • Plotting Bivariate Distributions
  • • Scatter plots in seaborn
  • • Pair plots in seaborn
  • • Heatmaps
  • • ggplot
  • • In-Class Discussion
  • • Types of Machine Learning
  • • Terminology: Features and Observations
  • • Representing Observations
  • • Terminology: Labels
  • • Terminology: Continuous and Categorical Features
  • • Continuous Features
  • • Categorical Features
  • • Common Distance Metrics
  • • The Euclidean Distance
  • • What is a Model
  • • Supervised vs. Unsupervised Machine Learning
  • • Supervised Machine Learning Algorithms
  • • Unsupervised Machine Learning Algorithms
  • • Choosing the Right Algorithm
  • • The scikit-learn Package
  • • Scikit-learn Estimators, Models, and Predictors
  • • Model Evaluation
  • • The Error Rate
  • • Confusion Matrix
  • • The Binary Classification Confusion Matrix
  • • Multi-class Classification Confusion Matrix Example
  • • ROC Curve
  • • Example of a ROC Curve
  • • The AUC Metric
  • • Feature Engineering
  • • Scaling of the Features
  • • Feature Blending (Creating Synthetic Features)
  • • The 'One-Hot' Encoding Scheme
  • • Example of 'One-Hot' Encoding Scheme
  • • Bias-Variance (Underfitting vs. Overfitting) Trade-off
  • • The Modeling Error Factors
  • • One Way to Visualize Bias and Variance
  • • Underfitting vs. Overfitting Visualization
  • • Balancing Off the Bias-Variance Ratio
  • • Regularization in scikit-learn
  • • Regularization, Take Two
  • • Dimensionality Reduction
  • • PCA and isomap
  • • The Advantages of Dimensionality Reduction
  • • The LIBSVM format
  • • Life-cycles of Machine Learning Development
  • • Data Splitting into Training and Test Datasets
  • • ML Model Tuning Visually
  • • Data Splitting in scikit-learn
  • • Cross-Validation Technique
  • • Hands-on Exercise
  • • Classification (Supervised ML) Examples
  • • Classifying with k-Nearest Neighbors
  • • k-Nearest Neighbors Algorithm
  • • Regression Analysis
  • • Simple Linear Regression Model
  • • Linear Regression Illustration
  • • Least-Squares Method (LSM)
  • • Gradient Descent Optimization
  • • Multiple Regression Analysis
  • • Evaluating Regression Model Accuracy
  • • The R2 Model Score
  • • The MSE Model Score
  • • Logistic Regression (Logit)
  • • Interpreting Logistic Regression Results
  • • Decision Trees
  • • Properties of Decision Trees
  • • Decision Tree Classification in the Context of Information Theory
  • • The Simplified Decision Tree Algorithm
  • • Using Decision Trees
  • • Random Forests
  • • Support Vector Machines (SVMs)
  • • 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)
  • • Global vs. Local Minimum Explained
  • • XGBoost
  • • Gradient Boosting
  • • A Better Algorithm or More Data?
  • • What is Python?
  • • Additional Documentation
  • • Which version of Python am I running?
  • • Python Dev Tools and REPLs
  • • IPython
  • • Jupyter
  • • Anaconda
  • • Python Variables and Basic Syntax
  • • Variable Scopes
  • • PEP8
  • • The Python Programs
  • • Getting Help
  • • Variable Types
  • • Assigning Multiple Values to Multiple Variables
  • • Null (None)
  • • Strings
  • • Finding the Index of a Substring
  • • String Splitting
  • • Triple-Delimited String Literals
  • • Raw String Literals
  • • String Formatting and Interpolation
  • • Boolean
  • • Boolean Operators
  • • Numbers
  • • Looking Up the Runtime Type of a Variable
  • • Divisions
  • • Assignment-with-Operation
  • • Relational Operators
  • • The if-elif-else Triad
  • • Conditional Expressions (a.k.a. Ternary Operator)
  • • The While-Break-Continue Triad
  • • The for Loop
  • • try-except-finally
  • • Lists
  • • Main List Methods
  • • Dictionaries
  • • Working with Dictionaries
  • • Sets
  • • Common Set Operations
  • • Set Operations Examples
  • • Finding Unique Elements in a List
  • • Enumerate
  • • Tuples
  • • Unpacking Tuples
  • • Functions
  • • Dealing with Arbitrary Number of Parameters
  • • Keyword Function Parameters
  • • The range Object
  • • Random Numbers
  • • Python Modules
  • • Creating a Runnable Application
  • • List Comprehension
  • • Zipping Lists
  • • Working with Files
  • • Reading Command-Line Parameters
  • • Accessing Environment Variables
  • • What is Functional Programming (FP)?
  • • Terminology: Higher-Order Functions
  • • Lambda Functions in Python
  • • Lambdas in the Sorted Function
  • • Regular Expressions
  • • Python Data Science-Centric Libraries

Training Materials

All Python Data Science training attendees receive comprehensive courseware.

Software Requirements

• Anaconda Python 3.6 or later

• Spyder IDE and Jupyter notebook (Comes with Anaconda)

Why Online Bootcamps

Develop skills for real career growth

Cutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills

Learn by working on real-world problems

Capstone projects involving real world data sets with virtual labs for hands-on learning

Learn from experts active in their field, not out-of-touch trainers

Leading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.

Structured guidance ensuring learning never stops

24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

FAQs

  • Python is the most popular programming language for Data Science. Python is widely used to perform data analysis, data manipulation, and data visualization. The advantages of using Python for data science are:
  • • Python offers access to a wide variety of Data Science libraries and it is the ideal language for implementing algorithms and the rapid development of applications in Data Science.
  • • Python is an object-oriented programming language with integrated dynamic semantics, used primarily for application and web development. The widely used language offers dynamic binding and dynamic typing options.
  • • Python is a high-level programming language with an enormous community. Its flexibility is quite useful for any issues related to application development in Data Science.

    The rapid evolution of learning methodologies, thanks to the influx of technology, has increased the ease and efficiency of online learning, making it possible to learn at your own pace. Simplilearn's Python Data Science course provides live classes and access to study materials from anywhere and at any time. Our extensive (and growing) collection of blogs, tutorials, and YouTube videos will help you get up to speed on the main concepts. Even after your class ends, we provide a 24/7 support system to help you with any questions or concerns you may have.

    Harvard Business Review has already named Data Scientist as the ‘Sexiest Job of the 21st Century.’ The statement is echoed in LinkedIn Emerging Jobs Report 2021 in which Data Science specialists are one of the top emerging jobs in the US with Python as one of its key skills. The job role has witnessed an annual growth of 35 percent for Data scientists and Data engineers.

    If you have prior coding experience or familiarity with any other object-oriented programming language, it will be easier for you to learn Python for Data Science. However, it is not compulsory.

    Python has simple syntax and is easy to understand. Knowledge of Java or C++ language helps in learning Python faster. This is because Python is also object-oriented and many of its prototypes are similar to Java. So you can easily migrate to Python with this comprehensive course.

    Python is used for a variety of applications and you don’t need to be familiar with all of its libraries and modules. Even if you know the basics of Python, this Data Science with Python certification covers the popular libraries of Python that are used in data science projects.