When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.
Like the Wall Street “quants” of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods.
That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century. It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years.
The supply, on the other hand, has been quite restricted. It is challenging to get the knowledge and abilities required to be recruited as a data scientist.
Lots of resources for learning Python are available online. Because of this, students frequently get overwhelmed by Python’s high learning curve.
It’s a whole new ball game in here! Step-by-step instruction is the hallmark of this course. Throughout each subsequent lesson, we continue to build on what we’ve previously learned. Our goal is to equip you with all the tools and skills you need to master Python, Numpy & Pandas.
You’ll walk away from each video with a fresh idea that you can put to use right away!
All skill levels are welcome in this course, and even if you have no prior programming or statistical experience, you will be able to succeed!
Essential Python Libraries for Data Science
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1Welcome to the course!
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2Introduction to Python
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3Course Materials
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4Setting up Python
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5What is Jupyter?
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6Anaconda Installation: Windows, Mac & Ubuntu
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7How to implement Python in Jupyter?
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8Managing Directories in Jupyter Notebook
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9Input/Output
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10Quiz 1
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11Working with different datatypes
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12Variables
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13Quiz 2
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14Quiz 3
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15Arithmetic Operators
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16Quiz 4
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17Quiz 5
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18Quiz 6
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19Comparison Operators
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20Logical Operators
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21Quiz 7
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22Quiz 8
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23Quiz 9
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24Conditional statements
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25Loops
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26Sequences: Lists
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27Sequences: Dictionaries
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28Sequences: Tuples
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29Quiz 10
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30Quiz 11
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31Quiz 12
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32Functions: Built-in Functions
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33Functions: User-defined Functions
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34Quiz 13
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35Quiz 14
Fundamental NumPy Properties
Mathematics for Data Science
Python Pandas DataFrames & Series
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50Basic NumPy arrays: zeros()
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51Basic NumPy arrays: ones()
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52Basic NumPy arrays: full()
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53Quiz 17
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54Adding a scalar
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55Subtracting a scalar
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56Multiplying by a scalar
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57Dividing by a scalar
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58Raise to a power
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59Transpose
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60Element wise addition
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61Element wise subtraction
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62Element wise multiplication
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63Element wise division
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64Matrix multiplication
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65Quiz 18
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66Statistics
Data Cleaning
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67What is a Python Pandas DataFrame?
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68What is a Python Pandas Series?
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69DataFrame vs Series
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70Creating a DataFrame using lists
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71Creating a DataFrame using a dictionary
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72Loading CSV data into python
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73Changing the Index Column
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74Inplace
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75Examining the DataFrame: Head & Tail
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76Statistical summary of the DataFrame
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77Slicing rows using bracket operators
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78Quiz 19
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79Indexing columns using bracket operators
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80Boolean list
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81Filtering Rows
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82Filtering rows using & and | operators
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83Filtering data using loc()
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84Quiz 20
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85Filtering data using iloc()
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86Quiz 21
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87Quiz 22
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88Adding and deleting rows and columns
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89Sorting Values
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90Exporting and saving pandas DataFrames
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91Concatenating DataFrames
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92groupby()