PYTHON FOR DATA ANALYTICS

ONLINE CERTIFICATE COURSE

OBJECTIVE

Everyone wants to succeed in business, but this digital world requires an in-depth understanding of the data behind your business. Whether you are a manager, a business analyst, a consultant, or a student, you need to possess the skills to be able to gain essential insights into your data through analytics.

As the top-ranked programming language, Python will allow you to analyze and create visualizations from your data to move your organization ahead—and you along with it. Whether you are a first-time programmer or someone with experience in other languages, the Python for Data Analytics certificate course will give you the foundation to do so with confidence.

  • 27% – is Python’s year-over-year-growth rate in usage.
    Source: Tech Republic
  • 40% of developers use Python and 25% want to learn it, according to Stack Overflow.
    Source: Economist

 

WHO IS THIS COURSE FOR?

  • Product managers & mid-level functional managers such as: Project Managers, Marketing Managers, Finance Managers, Portfolio Managers etc. interested in achieving a quick-start at the data science lifecycle, tools, and approaches
  • Software Programmers and Product Engineers looking to incorporate analytics to their apps
  • Data or Technical Business Analysts wanting to learn a powerful new tool for data analytics
  • Technology Consultants and Directors working on data analytics and advisory projects
  • Students, Researchers, Academicians interested in learning a programming language to build their own data models to analyze research data
  • Individuals seeking a career transition to data analytics

Python for Data Science Certificate

GET PROGRAM INFO

Take the first step to a Global Education

Your details will not be shared with third parties. Privacy Policy

Duration and Course Fee

  • Starts June 29, 2020
  • 3 Months
  • 4–6 hours per week
  • Course Fees $1,350

 

 

Faculty


Kristen Kehrer

Data Science Instructor at UC Berkeley Extension


Carmen Taglienti

Software Engineer and Systems Architect


Favio Vazquez

Physicist and Computer Engineer with a M.Sc. in Physics


Marianna Lamnina

Ph.D. in Cognitive Science from Columbia University

COURSE HIGHLIGHTS

  • 100 Recorded Video Lectures
  • null
    12 Live Online Teaching Sessions
  • null
    35 Application Assignments
  • null
    4 Quizzes
  • null
    25+ Discussions

SYLLABUS

  • Why Learn Data Science?
  • What is Data Science?
  • Essential Data Science Tools
  • The Data Science Lifecycle
  • Adopting a Data Scientist’s Mindset
  • Collaboration, Reproducibility, and Ethics
  • Introduction to Python
  • Running Jupyter Notebooks
  • How to use a Jupyter Notebook
  • Basic Data Types
  • Comparison and Logical Operators
  • Lists and Indexing
  • Advanced Indexing
  • Updating Data in a List
  • Introduction to Tuples
  • Introduction to Dictionaries in Python
  • Functions and Arguments
  • Methods
  • Writing User-Defined Functions Part 1
  • Writing User-Defined Functions Part 2
  • Conditionals: If Statements
  • Conditionals: While Loops
  • For Loops
  • Looping Through a Dictionary
  • Packages
  • Getting Started with NumPy Arrays
  • Getting Started with 2D NumPy Arrays
  • Looping over NumPy Arrays
  • Getting Started with Pandas: Creating DataFrames, Slicing & Filtering DataFrames
  • NumPy and Pandas: Statistical Tools
  • Functions Review
  • Global Scope vs. Local Scope
  • Nested Functions
  • Default and Flexible Arguments
  • Handling Errors and Exceptions
  • Writing Lambda Functions
  • Importing and Exporting Data
  • Introduction to Pandas Objects – Series, DataFrames, Common Functionality
  • Indexing and Selecting Data
  • Editing DataFrames: Setting Columns, Transforming Columns, Setting Data with loc
  • Combining DataFrames: Part 1
  • Reshaping DataFrames
  • Grouping and Aggregating in Pandas
  • Getting Started with Matplotlib and Popular Data Visualization Tools in Python
  • Simple Line Plots and Basic Graph Plots
  • Bar Plot
  • Histograms
  • Scatter Plot
  • Customizing Graphs
  • Line of Best Fit
  • Boxplots
  • Pair Plots
  • Time Series
  • Introduction to 3D Visualization
  • Exporting Visualizations
  • Probability vs. Statistics
  • Sampling
  • Random Variables
  • Probability distribution function
  • Normal Distribution
  • T distribution
  • Bernoulli Distribution
  • Confidence Intervals
  • Sample Size Determination
  • P values vs. Alpha
  • Basic Hypothesis Testing
  • Probability vs. Statistics
  • Sampling
  • Random Variables
  • Probability distribution function
  • Introduction to Exploratory Data Analysis
  • Descriptives, Frequencies, and Averages
  • Correlation
  • Visualizing and Plotting for Exploratory Data Analysis
  • Data Preprocessing
  • Exploratory Data Analysis Summary
  • Introduction to Linear Algebra for Data Science and Machine Learning
  • Matrices and Vectors in Python
  • Matrix Addition and Subtraction
  • Dot Product and Cross Product
  • Matrix Multiplication and Division
  • Transposition
  • Matrix determinant and Inverse
  • Span and Linear Independence
  • Eigenvalues and Eigenvectors
  • Singular Value Decomposition
  • Principle Component Analysis
  • Maximum Likelihood Estimation by Example

Duration and Course Fee

  • Starts June 29, 2020
  • 3 Months
  • 4–6 hours per week
  • Course Fees $1,350

 

 


Kristen Kehrer


Data Science Instructor at UC Berkeley Extension


Carmen Taglienti


Software Engineer and Systems Architect


Favio Vazquez


Physicist and Computer Engineer with a M.Sc. in Physics


Marianna Lamnina


Ph.D. in Cognitive Science from Columbia University

GET PROGRAM INFO

Take the first step to a Global Education

Your details will not be shared with third parties. Privacy Policy

Duration and Course Fee

  • Starts June 29, 2020
  • 3 Months
  • 4–6 hours per week
  • Course Fees $1,350

 

 

Faculty


Kristen Kehrer

Data Science Instructor at UC Berkeley Extension


Carmen Taglienti

Software Engineer and Systems Architect


Favio Vazquez

Physicist and Computer Engineer with a M.Sc. in Physics


Marianna Lamnina

Ph.D. in Cognitive Science from Columbia University

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