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 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

Lecture 20 Sort the index of a dataframe

Section 5: Lists

Lecture 21 How to sort the elements of a list using lambda functions

Lecture 22 How to remove some elements from a list at once

Lecture 23 How to create a list (sublist) that has some elements of another list

Lecture 24 Defining a list with numbers 1,2,3,..,9 using list comprehension

Lecture 25 How to print the first 5 and the last 5 elements of a list

Lecture 26 How to include the elements of another list into a list (extend versus append)

Lecture 27 How to remove all occurrences of an element from a list

Lecture 28 Difference between pop() and remove()

Lecture 29 List comprehension

Lecture 30 Slicing

Lecture 31 Enumerate the list (enumerate, index)

Section 6: Dataframe HOW-TOs

Lecture 32 How to return elements from a dataframe

Lecture 33 How to delete rows/columns from a dataframe using iloc, drop

Lecture 34 How to read the row /column index and values (df.values)

Lecture 35 How to show the max number of rows and columns of a dataframe

Lecture 36 How to create a copy of a dataframe

Lecture 37 How to correctly take a backup of a dataframe (copy() vs =)

Lecture 38 How to change specific values of a dataframe while leaving the rest unchanged

Lecture 39 Create a new column and populate with elements of another column of a dataframe

Lecture 40 How to change the order of the columns of a dataframe

Lecture 41 How to create a new row in a dataframe and fill it with values from other rows

Lecture 42 How to fill a new column with values 1,2,3,… (np.arange)

Lecture 43 How to use Pivot tables on Python

Lecture 44 How to rename rows and columns of a dataframe

Lecture 45 How to create a dataframe using a dictionary

Lecture 46 How to find the transpose of a dataframe

Lecture 47 Selecting rows and columns from a dataframe

Lecture 48 Copy-paste a row of a dataframe (np.repeat)

Lecture 49 How to sort the columns of a dataframe

Lecture 50 How to change the data type of a column of a dataframe

Lecture 51 How to select many rows (loc, arange)_

Lecture 52 How to delete many rows from a dataframe at once

Lecture 53 How to return the value under other columns, in the same row of a dataframe

Lecture 54 How to Iterate through the rows of a dataframe iteritems

Lecture 55 How to sort the values of a column sort_values()

Lecture 56 Populate a dataframe column using list, series arrays

Lecture 57 Format the values of a dataframe to percentage

Lecture 58 Define a dataframe with and without a dictionary

Section 7: Loops

Lecture 59 Prevent copying rows from a dataframe to dict (continue)

Lecture 60 Prevent duplicates while inputting a new row in a dataframe (next)

Lecture 61 Avoid duplicate entries using while & break

Lecture 62 continue, break, pass

Lecture 63 for - else

Lecture 64 while for equivalence

Lecture 65 While True

Lecture 66 While else

Section 8: multi - level (column) dataframes

Lecture 67 How to define a dataframe whose column index has many levels (headers)

Lecture 68 How to rename a column in a dataframe with many column index levels (rename)

Lecture 69 How to remove a level from a dataframe with many column levels

Lecture 70 Compressing all levels into 1 excel cell or showing them as is (merge_cells)

Lecture 71 How to print dataframe merged cells as unmerged in excel (startrow)

Lecture 72 How to iterate through the rows of a multi level dataframe (iteritems)

Section 9: Conditionals

Lecture 73 Inline if statement

Section 10: Logicals

Lecture 74 How to correctly write AND OR TRUE FALSE

Lecture 75 How to correctly write the NOT operator

Lecture 76 The De Morgan's Law. Many and, or, not statements

Lecture 77 Comparing objects of type int, str, float, bool with each other

Lecture 78 Type conversions: Int, Float, Str, Bool

Lecture 79 Combining NOT with empty lists and strings

Lecture 80 what it means for x to be none, empty list, empty string

Section 11: Tuples

Lecture 81 How to iterate through tuples. Different types of for-loops

Lecture 82 How to concatenate 2 tuples

Lecture 83 Defining a tuple

Lecture 84 Sorting a tuple

Lecture 85 Enumerating a tuple (enumerate, index)

Lecture 86 Find the frequency of elements in a tuple (count)

Section 12: NaN values

Lecture 87 How to remove NaN values by deleting rows or columns (dropna)

Lecture 88 Find if a dataframe has at least 1 missing value. And find their exact location!

Lecture 89 Using min_count to sum based on NaN values

Lecture 90 Manually place NaN values to dataframes

Lecture 91 Sum rows of a dataframe by ignoring NaN (skipna)

Lecture 92 Replace missing values with 0

Section 13: Python Implementation of Excel Functions

Lecture 93 Model the Vlookup Excel function on Python (map function)

Lecture 94 Model the SUMIFS function on Python

Lecture 95 Model the AVERAGEIFS function on Python

Section 14: Strings

Lecture 96 How to evaluate string expressions using eval ()

Lecture 97 Removing characters from end, start of a string (lstrip, rstrip)

Lecture 98 How to break a long sequence of characters in sets of characters (wrap)

Lecture 99 How to select part of a string (e.g. all string except last 3 characters)

Lecture 100 Use replace() to remove white spaces from a string.

Lecture 101 Find multiple occurrences of a subtext in a long string

Lecture 102 Selecting specific characters from a column using "str"

Lecture 103 How to replace text of a string using replace()

Section 15: Creating variables

Lecture 104 How to define variables using globals

Lecture 105 Multiple assignment

Section 16: Sets

Lecture 106 Define a set, add/remove elements

Lecture 107 Convert list string to a set

Lecture 108 Difference of two sets. Symmetric difference. Difference update

Lecture 109 Set comprehension

Lecture 110 Subset, superset, proper subset

Lecture 111 Intersection & union of two sets

Section 17: Series

Lecture 112 Editing strings inside series (series.str[])

Lecture 113 How to define a series object that has a constant value (pd.Series..)

Lecture 114 Selecting a column as a Series object versus as a Dataframe

Section 18: Numpy Arrays

Lecture 115 How to concatenate arrays (append)

Lecture 116 How to create equally spaced numbers linspace

Lecture 117 Reshaping the arrays (reshape)

Lecture 118 1D 2D 3D arrays from lists

Lecture 119 How to modify elements of an array

Lecture 120 How to use arange to create 1D and 2D arrays

Lecture 121 eye ones zeros. Instantly make arrays of constants

Lecture 122 Flattening an array (collapsing it to 1D) (flatten() )

Section 19: Functions

Lecture 123 Docstring

Lecture 124 Count how many times a function is called

Lecture 125 How to return many values from a function

Lecture 126 Default values for parameters

Lecture 127 A function calling another function

Section 20: Dates

Lecture 128 How to update a value in a DateTime index in a dataframe.

Lecture 129 Using the Workalendar package for country-specific Dates

Lecture 130 Use timedelta() for time conversaions

Section 21: Datatypes

Lecture 131 Use __name__ to find the datatype of an object

Lecture 132 How to check if the datatype of a variable is: int, float, str, NaN, Nonetype

Lecture 133 Datatype of every element of a dataframe: for loop, dtypes, astype()

Section 22: Dictionaries

Lecture 134 Define a dictionary and loop through it

Lecture 135 How to find the number of elements in a dictionary (len)

Lecture 136 Convert a dictionary into a list/set of keys/values. Find its unique values

Lecture 137 How to convert a dataframe to a dictionary (to_dict) and how to use it

Lecture 138 How to print the first 6 elements of a dictionary

Lecture 139 What it means to check if x is in dictionary

Lecture 140 How to convert a single value into a dictionary (all keys are this value)

Lecture 141 How to avoid errors when a key is not found in a dictionary (command: get)

Lecture 142 How to unite two dictionaries (double asterisk)

Lecture 143 Dictionary comprehension

Lecture 144 Delete a key from a dictionary

Lecture 145 Sort a dictionary

Section 23: Special types of dictionaries

Lecture 146 Default dictionary: how it works and why use it.

Section 24: Basic mathematics

Lecture 147 Trigonometry , infinite and pi

Lecture 148 regular/integer/modulo divisiob // %

Lecture 149 dot product of two arrays np.dot

Section 25: Errors (Exceptions)

Lecture 150 How to manually trigger errors based on user input (raise ValueError)

Lecture 151 Deal with errors via the Try-Except block

Lecture 152 The statement "finally"

Section 26: Random package

Lecture 153 random choice

Lecture 154 randint

Lecture 155 randrange

Lecture 156 random.random, random.seed

Lecture 157 random.sample (sample without replacement)

Section 27: Bonus

Lecture 158 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