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Become aData ScientistCourse overview

RMOTR courses offer the shortest path to become a Data Scientist. Learning in a human-driven environment, keeping yourself accountable with two LIVE classes per week and developing unique industry ready projects.

PYTHONDATA SCIENCECOURSEPython is the most popular Programming Language for Data Science (overtaking R in 2017) and it’s also the most popular introductory teaching language at top U.S. Universities.

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Average Salary

$110,000Median Base Salary reported by GlassDoor

Best Job in America

» 1st «3rd year in a row considered the best job in America

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Python*

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Other

Python vs R vs Otherfor Analytics & Data Science,2014-17

Python vs R vs Other for Analytic, Data Science, and Machine Learning, 2014-17 (kdnuggets.com)

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FIRST 2CLASSES

UNLIMITEDMENTORING

FULLCONTENT

TRY IT FOR FREE

Your free trial period includes 2 Live classes + full access to videos and exercise and unlimited mentoring. Cancel at any time. Sign up now: https://rmotr.com/dsc

THE 4 KEY ASPECTS OF DATA SCIENCE

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1. Data AnalysisLearn to analyse your data to find valuable insights and turn raw data into actionable information. Clean it, rearrange it, and modify it to suit your needs, regardless of the original shape or source.

Project Spotlight: Marketing Report

2. VisualizationsCreate compelling visualizations toCommunicate your findings.Understand the differences between different visualization libraries and chart types, and when to use each one.

Project Spotlight: Census Analysis

4. Machine LearningThe course finishes with a week on Machine Learning, to introduce you to the most important concepts, using scikit learn and Tensor Flow.

Project Spotlight: Hotdog vs Not Hotdog

3. Time SeriesLearn how to handle time indexed data, analyze trends and forecasting.Everything can be thought as a time series: biological data, financial data, healthcare data, etc.

Project Spotlight: Financial Analysis

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seaborn

Bokeh

AdvancedPython

In this module you'll learn two key programming paradigms to manipulate data: Object Oriented and Functional Programming. We'll also introduce file manage-ment, databases and an overview of web programming and HTTP.At the end of this module you'll be comfortable designing efficient algorithms combining OOP and Functional paradigms while interacting with I/O (files and databases).

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[UNIT 1] /

- Intro to OOP- Define Classes- Create Objects- Understand methods and attributes- Work with `self`

[UNIT 2] /

- Advanced OOP concepts - Work with class and static methods- Inheritance and polymorphism

[UNIT 3] /

- File handling- Work with JSON, CSV or XML files- Python pickle- Functional programming- List comprehensions- Iterators and Generators

[UNIT 4] /

- Database Management - Creating and modifyingDatabase tables- Querying with SQL- Working with HTTP- Pulling data from APIs

m o d u l e

Intro to PythonWe start with an in-depth introduction to Programming and Python. You'll also learn about the tools required to work efficiently in a Python environment.Many fundamental concepts of programming are explained in this module: data types, collections, testing, etc.At the end of this module you'll be confortable setting up your own Python environment, working with the fundamental data types and collections, debugging errors and interacting with Github to show your code to the world.

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[UNIT 1] /

- Intro to Python- Intro to Control Flow- Fundamental Data Types

[UNIT 2] /

- Advanced Control Flow- Introduction to collections- Functions, arguments and invocations

[UNIT 3] /

- Advanced Collections- Dictionaries and sets- Performance of collections- Advanced function invocation and usage.

[UNIT 4] /

- Modules and packages- Exception handling- Important stdlib modules

m o d u l e

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syllabus

AppliedData Science

Now that you're comfortable with the programming fundamentals, collections, data types and paradigms, we'll put all that knowledge into practice with advanced Data Science applications.Learn how to analyze data with Pandas and visualize it with Matplotlib, Seaborn and Bokeh.Learn how to manipulate Time Series for time-indexed data, specially analysis of financial data and reports: moving averages, ARMA models and forecasting.We'll finish with an introduction to Machine Learning using Scikit Learn and Tensor Flow focusing on Feature Engineering and Analysis.

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[UNIT 1] /

- Introduction to Numpy and Pandas- Visualizations with Matplotlib and Seaborn.- Statistical analysis to understand our data- Data cleaning and normalization.

[UNIT 2] /

- Advanced Pandas models- Hierarchical indexing- Data Wrangling and transformations- Advanced visualizations

[UNIT 3] /

- Work with Time Series- Advanced indexing- Financial models and forecasting- Rolling, expanding windows- Grouping and aggregations

[UNIT 4] /

- Introduction to Machine Learning- Intro to Regressions- Linear and logistic regression using Scikit Learn- Intro to Classification- Classification with K nearest Neighbors- Decision Trees and Random Forest

m o d u l e Too Basic for you?

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