Data Science Code That Appears All The Time At Workplace.

Learn exactly all the Programming (Python) skills that are needed all the time at workplace. Each video = 1 skill.

What you'll learn
In practice, 1% of Python is used 99% of the time. This course focuses on this 1%.
Great for Quants/ Economists, Data Scientists/ Software Engineers: the skills shown here, come up all the time.
This is your Help Resource when you are under heavy pressure!
You will not need to google-search to find answers again. Everything you need is in this course.
The subtitles are manually created. Therefore, they are fully accurate. They are not auto-generated.
Part of the giannelos dot com official certificate for high-tech projects.
Requirements
The only prerequisite is to take the first course of the "giannelos dot com" program , which is the course "Data Science Code that appears all the time at workplace".
Description
What is the course about:This online course focuses on the part of Data Science that keeps appearing all the time in any workplace. Save time and learn only what you will need 99.9% of the time. The idea is that this course can be your encyclopedia. When you don't remember how something is done on Python, you just resort to this course. You might have realized that Python and Data Science are like an ocean; you can keep learning and learning, but in the end, at work, you will need to perform, as quickly as possible. And this comes down to knowing the skills, the techniques, taught in this course! ​There are no prerequisites.Every topic is analyzed in depth so you can feel confident about what you learn.Every video is a building block. Once you know these building blocks you can do anything with data science. This course corresponds to the official certificate for the famous giannelos dot com program for high-tech projects.  Who:I am a research fellow at Imperial College London, and I have been part of high-tech projects at the intersection of Academia & Industry for over 10 years, prior to, during & after my Ph.D. I am also the founder of the giannelos dot com program in data science.Doctor of Philosophy (Ph.D.) in Analytics & Mathematical Optimization applied to Energy Investments, from Imperial College London, and Masters of Engineering (M. Eng.) in Power Systems and Economics. Important:Prerequisites: The course Data Science Code that appears all the time at Workplace.Every detail is explained, so that you won't have to search online, or guess. In the end, you will feel confident in your knowledge and skills. We start from scratch so that you do not need to have done any preparatory work in advance at all.  Just follow what is shown on screen, because we go slowly and explain everything in detail.

Overview

Section 1: Introduction

Lecture 1 Overview

Lecture 2 Full analysis

Section 2: Installing the necessary software

Lecture 3 Install Python

Section 3: Interacting with data in external sources

Lecture 4 How to read an xlsx file

Lecture 5 How to skip reading some rows when reading a dataframe

Lecture 6 How to read a specific sheet from an excel file into a dataframe

Lecture 7 How to set the index of a dataframe upon reading it

Lecture 8 How to read specific columns from an excel file into a dataframe (usecols)

Lecture 9 How to read data from World Bank's online database

Lecture 10 How to send many dataframes into the same excel file (xlsx) in different sheets

Lecture 11 Sending a dataframe to a csv file

Lecture 12 How to hide warnings that Python produces. And how to trigger manually warnings

Lecture 13 How to read only some rows from the top/bottom of a dataframe(nrows, skipfooter)

Lecture 14 How to check if an Excel cell is empty

Lecture 15 How to see the version of the packages we have installed

Section 4: Index of a dataframe

Lecture 16 Columns and index: reset_index, set_index, drop=T

Lecture 17 How to change the index name of a dataframe

Lecture 18 How to find the row index & column index of any element of a dataframe

Lecture 19 How to enumerate the rows (enumerate) and use it in for loops

Section 5: Lists

Lecture 20 How to sort the elements of a list

Lecture 21 How to remove some elements from a list at once

Lecture 22 How to create a list (sublist) that has some elements of another list

Lecture 23 Defining a list with numbers 1,2,3,..,9 using list comprehension

Lecture 24 How to print the first 5 and the last 5 elements of a list

Lecture 25 How to include the elements of another list into a list (extend versus append)

Lecture 26 How to remove all occurrences of an element from a list

Lecture 27 Difference between pop() and remove()

Section 6: Dataframe HOW-TOs

Lecture 28 How to return elements from a dataframe

Lecture 29 How to delete rows/columns from a dataframe using iloc, drop

Lecture 30 How to read the row /column index and values (df.values)

Lecture 31 How to show the max number of rows and columns of a dataframe

Lecture 32 How to create a copy of a dataframe

Lecture 33 How to change specific values of a dataframe while leaving the rest unchanged

Lecture 34 Create a new column and populate with elements of another column of a dataframe

Lecture 35 How to change the order of the columns of a dataframe

Lecture 36 How to create a new row in a dataframe and fill it with values from other rows

Lecture 37 How to fill a new column with values 1,2,3,… (np.arange)

Lecture 38 How to use Pivot tables on Python

Lecture 39 How to rename rows and columns of a dataframe

Lecture 40 Going through every element with loc, iloc and nested for loop

Lecture 41 Copy-paste a row of a dataframe (np.repeat)

Lecture 42 How to sort the columns of a dataframe

Lecture 43 How to change the data type of a column of a dataframe

Lecture 44 How to prevent reading a row of a dataframe using: iterrows, continue and a list

Lecture 45 How to select many rows (loc, arange)

Lecture 46 How to not allow duplicate values while inputting a new row in a dataframe

Lecture 47 How to return the value under other columns, in the same row of a dataframe

Lecture 48 How to Iterate through the rows of a dataframe iteritems

Lecture 49 How to delete many rows from a dataframe at once

Lecture 50 How to correctly take a backup of a dataframe (copy() vs =)

Section 7: multi - level (column) dataframes

Lecture 51 How to define a dataframe whose column index has many levels (headers)

Lecture 52 How to rename a column in a dataframe with many column index levels (rename)

Lecture 53 How to remove a level from a dataframe with many column levels

Lecture 54 Compressing all levels into 1 excel cell or showing them as is (merge_cells)

Lecture 55 How to print dataframe merged cells as unmerged in excel (startrow)

Lecture 56 How to iterate through the rows of a multi level dataframe (iteritems)

Section 8: Conditionals

Lecture 57 Inline if statement

Section 9: Logicals

Lecture 58 How to correctly write AND OR TRUE FALSE

Lecture 59 How to correctly write the NOT operator

Lecture 60 The De Morgan's Law. Many and, or, not statements

Lecture 61 Comparing objects of type int, str, float, bool with each other

Lecture 62 Clarify difference between is =

Section 10: Tuples

Lecture 63 How to iterate through tuples. Different types of for-loops

Section 11: NaN values

Lecture 64 How to remove NaN values by deleting rows or columns

Lecture 65 Find if a dataframe has at least 1 missing value. And find their exact location!

Lecture 66 Using min_count to sum based when there are NaN values

Lecture 67 Manually place NaN values to dataframes

Lecture 68 How to sum rows of a dataframe by ignoring or considering NaN (skipna, replace)

Section 12: Python Implementation of Excel Functions

Lecture 69 Model the Vlookup Excel function on Python (map function)

Lecture 70 Model the SUMIFS function on Python

Lecture 71 Model the AVERAGEIFS function on Python

Section 13: Strings

Lecture 72 How to evaluate string expressions using eval ()

Lecture 73 Removing trailing characters (rstrip)

Lecture 74 How to break a long sequence of characters in sets of 4 characters (wrap)

Lecture 75 How to select part of a string (e.g. all string except last 3 characters)

Lecture 76 Remove white space / blanks from a string (replace()

Lecture 77 How to search for a subtext across a list of strings

Lecture 78 Selecting specific characters from a column using "str"

Lecture 79 How to replace characters or words from inside a string

Section 14: Datatypes

Lecture 80 Use __name__ to find the datatype of an object

Lecture 81 Type conversions: Int, Float, Str, Bool

Lecture 82 Combining NOT with empty lists and strings

Lecture 83 How to check if the datatype of a variable is: int, float, str, NaN, Nonetype

Lecture 84 Datatype of every element of a dataframe: for loop, dtypes, astype()

Lecture 85 what it means for x to be none, empty list, empty string

Section 15: Creating variables

Lecture 86 How to define variables using globals

Section 16: Sets

Lecture 87 Comparing the index of two dataframes using sets

Section 17: Dictionaries

Lecture 88 How to find the number of elements in a dictionary (len)

Lecture 89 How to convert a dictionary into a list/set of keys/values.

Lecture 90 How to convert a dataframe to a dictionary (to_dict) and how to use it

Lecture 91 How to print the first 6 elements of a dictionary

Lecture 92 What it means to check if x is in dictionary

Lecture 93 Default dictionary (part 1)

Lecture 94 Default dictionary and lambdas

Lecture 95 How to convert a single value into a dictionary (all keys are this value)

Lecture 96 How to avoid errors when a key is not found in a dictionary (command: get)

Lecture 97 How to have the same value while the keys differ

Lecture 98 How to unite two dictionaries (double asterisk)

Lecture 99 How to create a dataframe using a dictionary

Section 18: Numpy Arrays

Lecture 100 How to concatenate arrays

Section 19: Series

Lecture 101 How to define a series object that has a constant value

Section 20: Dates

Lecture 102 How to update a value in a DateTime index in a dataframe.

Lecture 103 Using the Workalendar package for country-specific Dates

Lecture 104 Use timedelta() for time conversaions

Section 21: Functions

Lecture 105 Count how many times a function is called

Lecture 106 Another way: Number of times a function is called through lists

Section 22: Bonus

Lecture 107 Extras

Entrepreneurs,Economists,Quants,Members of the highly googled giannelos dot com program,Investment Bankers,Academics, PhD Students, MSc Students, Undergrads,Postgraduate and PhD students.,Data Scientists,Energy professionals (investment planning, power system analysis),Software Engineers,Finance professionals