The Complete Data Analyst Bootcamp – FAQ

Preface

IMPORTANT PLEASE DONT SKIP THIS PART!

Here we will describe the process you need to go through whenever you encounter an error in programming. Please read the following two pages carefully, as they will surely save you a lot of time in the long run!

A general note on problem-solving:

Even the best programmers out there find it nearly impossible to successfully create the complex code delivering the desired output the first time they write it. So, we can say coding is all about trying, stumbling upon an error, reading the obtained error message, finding a solution, and then applying it. Over and over again. In fact, this process is repeated infinitely.

Therefore, as a programmer, you need to develop the skill to quickly and efficiently identify and solve problems that occur while you are coding.

What should I do when I encounter a coding error?

With this question in mind, we will take you through the steps you should follow each time you encounter an error:

1) Read the error message carefully.

Every time you make a mistake, you will obtain an error message. Sometimes, this message will be quite specific; you will be able to immediately spot the mistake and correct it. Other times, though, it will sound more general. Then, it will be your job to find where the error stems from.

2) If you encounter an error while you are replicating code youve seen in a lecture, please re-watch that video or at least the parts about the query you are trying to execute. Remember that every symbol and letter could make a difference! So dont overlook the details they might be the cause of this error!

3) If the error occurs while you are trying to solve an exercise, please carefully check the notebook files containing the corresponding solution.

4) In some situations, the 3 steps described above may not help. Or you may still have a question, even though youve managed to solve the error. In that case, please search among the existing threads in the Q&A section. If you cant find the answer to your question, you can post a new one there.

How can I speed up the process of solving the encountered problems?

1) While coding in Python, the order in which you execute the code cells matters. So, please double-check to make sure you have executed all cells in the order shown in the lectures or the notebook files.

2) Keep this FAQ sheet open, as it may contain the answer you are looking for. Use Ctrl + F to find a keyword related to the problem you are trying to solve quickly.

Questions related to Udemy and the smooth taking of the course

Apart from questions related to coding in Python, you may wonder how to load the notebooks located in the resources sections of the lectures, or how to obtain a certificate at the end of the course. We will deal with such questions in the next section.

Use the FAQ sheet, the Q&A, and good luck while taking the course!

General:

1. How can I open the exercise files and the *.ipynb files in general?

a. Keywords: open, *.ipynb, exercise file, lecture file, solution file

The notebook files (*.ipynb), located in the resources sections of the course lectures, cannot be opened by double-clicking on them. You need to start Jupyter (or any other environment were using, like Spyder) and load the notebook files as shown in Section 3, Lecture 13 Jupyters Interface Intro to Using Jupyter. Please refer to this video for a detailed explanation on how this is done step-by-step.

Then, you can apply this technique to any of the notebook files attached to the lectures.

2. Ive completed the course, but the progress bar isnt showing 100%. What should I do?

Keywords: course completion, 100%

Sometimes students tell us that they have completed the course but they continue to see their progress as less than 100%.

If you are experiencing the same problem, please check if there is any video that you have missed watching or have not finished for some reason. In both cases, Udemy may have not recorded the time you’ve spent on these lectures.

If the issue persists, please contact Udemy Support, so that your 100% would be displayed and you could obtain your certificate!

https://support.udemy.com/hc/en-us

3. How can I get a certificate for completing the course?

Keywords: complete, certificate, Udemy

Please refer to this article which thoroughly explains how to attain your certificate of completion!

https://support.udemy.com/hc/en-us/articles/229603868-Certificate-of-Completion

4. Why is my quiz/video not loading/rendering?

Keywords: quiz, video, not loading, not rendering

Unfortunately, such issues occur from time to time.

You can try and refresh the page several times. If this does not solve your problem, you can wait for a while or contact Udemy support at:

https://support.udemy.com/hc/en-us

5. I have downloaded and installed Anaconda but I cant find and start Jupyter. What should I do?

Keywords: install, Anaconda, launch, start, Jupyter

There are three ways to start Jupyter.

1) Run Jupyter Notebook from the shortcut found in the Start menu (this is the option shown in the video).

2) Open Anaconda Prompt from the start menu and execute jupyter notebook

3) Open the Anaconda Navigator from the start menu and press the Launch button under the Jupyter icon

If 1) does not work, Anaconda Prompt will usually tell you what the error is, so you can look for a specific solution.

Please reinstall Anaconda in case none of these options works.

Mac users:

1) Open Terminal

2) Enter the startup folder by executing cd/folder_name

3) Execute jupyter notebook

6. How can I download all the course resources?

Keywords: all resources, download

Well, read above!

If you have downloaded the PDF, refer to Section 1, Lecture 3, where we explain how to download all the materials for the course.

For your convenience, here is a link to the associated lecture. https://www.udemy.com/course/the-data-analyst-course-complete-data-analyst-bootcamp/learn/lecture/23005538/

7. Why do we stick to conventions in Python? (e.g. import pandas as pd)

Keywords: conventions, Python, packages, pandas

An example of a convention is calling the web data web or wb. In our course, we obviously stick to the second one.

But in terms of Python code, what does wb refer to? It refers to the data part of the pandas-datareader package, right? That’s why we begin our code with

from pandas_datareader import data as wb.

Having executed this line of code in your session (session meaning the period in which you will keep your notebook and Jupyter open), Python will know that wb refers to the data part of the pandas-datareader package. This is nothing but a large chunk of code where certain functions and methods have been specified.

Therefore, wb.DataReader() is simply an indication where Python should search for a method called .DataReader().

Other conventions are plt for pyplot matplotlib code, np for numpy, and pd for pandas. It is a sign of good practice to stick to conventions such as np, plt, wb. That’s why we use them in the course and advise you to use them, too. Additionally, if youre ever looking for some help in StackOverflow, most (if not all) solutions align with these conventions. Thus, the more you know and apply them, the better youd understand somebody elses script (code).

Note: Methods, on the other hand, have their fixed names, and you cannot really use an alias for them. You’d have to create a new function or method, based on the initial method, and give it a name you like. However, this is not something people normally do. Therefore, it is better to try to remember the method or function name you need.

8. I opened the Resources link accompanying the lecture, but I cant make out what each file does. What files do I need for this lecture/section/exercise?

Keywords: template, complete, notebook. .ipybn, .csv, exercise, solution, file

In the Resources link accompanying this lecture/section, you’ll find all the additional files you need to replicate the code in the video.

*-Template.ipynb – These are “empty” Notebooks you can use to code along as we go through the lectures.

*-Complete.ipynb – Filled-out versions of the code we typed out over several lectures (or a full section) which you can use to check what we did, how we did it and why we did it. There are additional comments to help you further understand how everything works.

*-Exercise.ipynb – Notebooks which have some code filled out, but are mainly “empty” and only contain tasks/problems/exercises for you to replicate and test how well you understood the previous lectures.

*-Solutions.ipynb – Notebooks which contain one possible solution to the tasks assigned in the *-Exercise.ipynb files.

*.csv – Datasets which we will most likely import, manipulate and analyze in Jupyter.

Coding in Python:

1. I have all my Python notebooks and data files in the same directory but cannot load them. What should I do?

Keywords: import file, CSV, directory, full path

This is an issue that occurs due to your specific Anaconda installation. The easiest way to solve this is to reinstall Anaconda. However, we recommend that you use the absolute path of a file when loading the data.

Note: We can use the full path, even if our notebooks (*ipynb) and data files (*csv) are in the same directory. In that case, even if we move the Python notebook to a different folder (directory) on our machine, the code should still run without any errors.

Thus, you can write:

data = pd.read_csv(ABSOLUTE_PATH/real_esate_price_size.csv)

To me this looks like:

data = pd.read_csv(‘C:/Users/365/Desktop/The Data Science Course 2018 All Resources/Part_4_Advanced_Statistical_Methods_(Machine_Learning)/S27_L142/real_estate_price_size.csv’)

In your case, you can find that by opening the folder containing the files and copy-pasting the path.

Once you copy-paste this path, you should CHANGE ALL SLASHES from backward to forward. This means that if your path is C:UsersYOUR_COMPUTER_NAMEDesktop… , you should make it look like: C:/Users/YOUR_COMPUTER_NAME/Desktop/

Note that, due to the standards for referencing paths, in Python, instead of a backward slash ( ), you should use a forward slash ( / ).

2. What is the difference between a function and a method?

Keywords: method, function

Methods are called on specific instances of a given class (e.g. array_a.sort()), whereas functions dont. We call functions and provide the inputs within the parentheses (e.g. np.sort(array_a)).

Syntax-wise, functions usually appear at the start of the line, while methods show up at the end, and require a ..

Since methods always ever refer to a specific object (e.g array_a), they sometimes store the output in place of the original variable. In other words, methods can delete the initial contents of the variable and store the output in their place. However, this varies from method to method (some even have inplace arguments which can be set to either True or False), so you its a good idea to quickly check the documentation before using a method. In contrast, functions usually dont overwrite the original variable, so make sure to store their outputs if you plan on using them later on.

3. Why am I getting the error object not callable?

Keywords: object not callable, attribute, method

This occurs when we accidentally use method syntax (e.g. variable_1.shape()) to refer to an attribute (variable_1.shape).

When this happens, removing the parentheses should usually resolve the error.

4. What is the difference between load and import?

Keywords: load, import

Loading a dataset implies its already properly formatted, and we can directly use it to compute mathematical operations. However, importing a dataset means we need to clean and preprocess it first.

Note: We can still clean and preprocess a loaded dataset, if we wish to. However, we must always clean and preprocess a dataset were importing.

5. What is the difference between an error and a warning?

Keywords: error, warning

An error message means were not able to run a specific line of code, so our program crashes. There are plenty of actions which can lead to an error, including: incorrect syntax, inappropriate type conversion (casting), mismatching shape/size of the input variables, runtime errors caused by infinite loops/inefficient code, etc. These all stop the code from compiling and prematurely terminate the script.

In contrast, warning messages are just that warnings. They dont prevent our code from running, but bring attention to a non-vital issue we might be overlooking. Examples of errors might be: overwriting columns of a series without using the .loc() or .iloc() methods, feeding empty lists/arrays/series to a function or method, using a function or method which will be removed in future versions of a package etc. The purpose of errors is to notify the user that there exist inefficiencies in the code. However, since they dont affect the output, we can even suppress them by writing the following:

6. Im examining the results of a function/method, but the numbers look weird (e.g. 9.4529600e+03). What does this mean and how do I read the results?

Keywords: scientific notation, e

This is called scientific notation and we often see it among many programming languages. The purpose is to provide greater precision when were working with very large/small numbers.

The true value of any such numbers equals the value to the left of e, multiplied by 10, to the power of the value on the right side of e.

Hence, 9.4529600e+03= 9.4529600 * 10^3 = 9.4529600 * 1,000 = 9,452.96.

If the number on the right side of e is negative (e.g. 5.67e-04), then were diving by 10 to said power.

For example, 5.67e-04 = 5.67 * 10^(-4) = 5.67/(10^4) = 5.67/10,000 = 0.000567.

If we prefer to see the actual values on screen, rather than their corresponding scientific notation, we can suppress this type of notation. One way to achieve this is by calling NumPys set_printoptions() function and set the suppress argument to True.

Note: To use this function, we must also import numpy as np beforehand.

NumPy:

1. What is the difference between an array and an ndarray?

Keywords: array, ndarray

There are different array types across various programming languages. Ndarray is the most well-known and widely used array class in Python. It is part of the NumPy package, works elementwise and extremely computationally stable, so we often use ndarrays to compute mathematical operations.

NumPy also contains character arrays, but they are only left for backwards compatibility with legacy code. If we ever need to store text data in a NumPy array, well do so by using an ndarray, where every individual element is a string.

2. Whenever I display larger arrays with more than 6 columns, the values of each 1-D array are displayed on several lines, so the first and 6-th column overlap. What can I do to make this more legible?

Keywords: display large arrays, overlapping columns

Whenever were using NumPy and want to make our outputs easier to read and more coherent, we can use the set_printoptions() function.

By calling this set_printoptions() and setting the linewidth argument to a value like 100, 150, or even 200, were increasing the number of elements we want Python to fit in a single line of outputs.

Now, for the remainder of this session (kernel), Python will fit 100 elements of the output on a line, before moving to the next.