Python for Data Science: The Journey

Become a Data Science Expert in Python with NumPy and Pandas.

Master NumPy & Pandas.

Starting with the basics, ending at mastery. We've got you covered every step of the way.

Data Analysis

Data Cleaning

Data Wrangling

Vectorization

Date Fields

NumPy

Pandas

DataFrames & Series

N-Dimensional Arrays

Aggregate Functions

Exploratory Data Analysis

JOIN / MERGE

Prerequisites: Basic understanding of Python.

Get a Certificate That Means Something.

Upon completion of this course, you will receive Professional Certification from ML NOW to add to your Resumé and LinkedIn. Our certificates are signed by CEO and Founder Greg Hogg, who has a combined following of over 100,000 across platforms such as YouTube and LinkedIn.

The Best Learning Environment.

Our state-of-the-art platform gets you coding immediately, right here in the browser. It's been carefully optimized to bring you a fast, enjoyable, and headache-free learning experience. We've even fine-tuned and incorporated ChatGPT into the platform to help you code faster.

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Our Content is Unmatched.

We are proud of our commitment to true quality. Enjoy 40+ Industry-Leading Lessons, 20+ Knowledge Recalls, 5 Final Assessments, and an Exclusive Portfolio Project that will make your application stand out from the crowd.

The Ultimate Portfolio Project.

Our World-Class Data Science Project is easily shareable with employers and will position you as a unique asset among applicants. We utilize Python's Flask library to create and deploy an AirBnB Analysis Web App, hosted on the PythonAnywhere Anaconda Cloud.

One Subscription to Rule Them All.

Let All Access be your gateway to the Entire ML NOW Line of Products. Master Python, SQL, Data Science, and Machine Learning, all from just one membership.

And finally, our Course Material.

Chapter 1: Introduction to Pandas

Get introduced to Pandas by learning how to construct, query, and manipulate DataFrame and Series Objects: The 2 Fundamental Data Structures in Pandas.

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1: Exploring Pandas ... Let's check out those cute fluffy bears.

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2: Recall 1

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3: Pandas DataFrames ... They're basically just tables. Really awesome tables.

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4: Recall 2

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5: Pandas Series ... Each column and each row is a Series.

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6: DataFrame.loc[ ] ... Targeting specific rows.

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7: Recall 3

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8: Useful Utilities ... An assortment of awesome Pandas stuff.

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9: Final Assessment

Chapter 2: Intermediate Pandas

Perform complex Data Analysis and Wrangling in Pandas using techniques such as Apply, Lambdas, and Loops. Plus, learn how to deal with missing data / NULL values.

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1: Intermediate Pandas ... We'll get more used to the bears.

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2: Looping through a DataFrame ... Important to know, but it's pretty computationally slow.

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3: Manual Analysis ... Keeping it old-school for now.

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4: DataFrame Analysis ... Automation... s p o o k y

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5: Recall 1

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6: DataFrame.describe( ) ... It's better than it sounds.

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7: Recall 2

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8: Operations on a Series ... Data wrangling at its finest.

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9: Apply ... Custom transformations. Common ETL stuff.

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10: Recall 3

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11: DataFrame.apply( ) ... More generic transformations.

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12: Recall 4

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13: Looping through columns ... It really just loops through the names.

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14: Missing Data ... Where'd my data go?

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15: Filtering DataFrames ... Basically SELECT in SQL.

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16: Recall 5

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17: DataFrame Sorting ... Order up!

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18: Final Assessment

Chapter 3: Advanced Pandas

Unleash your inner Panda Bear using advanced concepts such as Index Manipulation, Merge, Date Programming, and learn the essential difference between In-Place and Return.

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1: Group By ... Group up those rows!

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2: Index ... The far left column, the one with the IDs.

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3: Recall 1

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4: Concat ... Con-kitty

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5: Recall 2

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6: Merge ... Basically an SQL Join.

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7: Recall 3

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8: Dates ... Programmers hate these things for good reason. But we need em.

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9: In-Place vs Return ... Know the difference!!!

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10: Recall 4

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11: Final Assessment

Chapter 4: Introduction to NumPy

Gain an introduction to NumPy by learning about N-Dimensional Arrays, Shape and Reshape, and essential operations on Arrays such as Indexing and Slicing.

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1: Introduction to NumPy ... NumPy is tricky. Take your time with it.

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2: NumPy Arrays ... [1 2 3], not [1, 2, 3]

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3: Recall 1

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4: N-Dimensions ... How many dimensions? N-many!

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5: Array Shape ... The arrangement of the items.

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6: Recall 2

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7: Array Reshape ... If you don't like the shape, just change it already.

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8: Recall 3

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9: Array Indexing and Slicing 1 ... Pretty similar to list stuff.

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10: Recall 4

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11: Array Indexing and Slicing 2 ... Still similar to list stuff... just more general.

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12: Recall 5

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13: Mathematical Operations on Arrays ... Add 'em divide 'em... do whatever you want with 'em.

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14: Final Assessment

Chapter 5: Advanced NumPy

Become a NumPy Master by performing advanced techniques such as Broadcasting, Concatenation, Aggregate Functions, and Vectorization to unlock the full capabilities of the framework.

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1: Operations on Multiple Arrays ... [1 2 3] + [2 3 4] == [3 5 7]

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2: Recall 1

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3: Operations Between Different Arrays ... Now it gets a little weird.

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4: Recall 2

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5: Modifying NumPy Arrays ... Very similar to how lists do it.

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6: np.arange( ) ... Helps us build arrays quickly.

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7: np.linspace( ) ... Evenly spaced numbers in a range.

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8: Recall 3

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9: Array Concatenation 1 ... If you can't beat em, join em.

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10: Array Concatenation 2 / Axis ... If you still can't beat em, still join em.

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11: Recall 4

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12: Aggregate Functions ... Sums, averages ... all the common stuff.

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13: Recall 5

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14: Random Arrays ... Not completely random... statistically random.

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15: Vectorization / Parallelization ... Understand this lesson. Please. Please?

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16: Final Assessment