Future Data Science(FDS)

Machine Learning Engineer Now, after that last point above, is where a machine learning engineer comes in. The main function is to put that model into production. A data science model can be quite static sometimes, and an engineer can help to automatically train and evaluate that same model. They would then insert the predictions back into the data warehouse/SQL tables for your company. After that, a software engineer and UI/UX designer will display the predictions into a user interface — if necessary. As you can see, the whole process from business problem to solution in a visible, easy to use format, is not just the responsibility of a data scientist (however, yes, some data scientists can do all x amount of roles). The role of a machine learning engineer can be also named ML ops (machine learning operations). A summary of their workflow would be something like this:

A. pkl_file of data science model

B. storage bucket (GCP — Google Cloud Composer)

C. DAG (for scheduling the trainer and evaluator of the model)

D. Airflow (visualizes the process — ML pipeline)

E. Docker (containters and virtualization)

At first, perhaps data science and machine learning could be seen as interchangeable titles and fields; however, with a closer look, we realize machine learning is more-so a combination of software engineering and data engineering than data science. In the next post, I will outline where the fields do and do not cross over.

11 Deep Learning With Python Libraries and Frameworks ❓Asked by one of the members 1⃣ TensorFlow Python. TensorFlow is an open-source library for numerical computation in which it uses data flow graphs. ... 2⃣ Keras Python. A minimalist, modular, Neural Network library, Keras uses Theano or TensorFlow as a backend. ... 3⃣ Apache mxnet. ... 4⃣ Caffe. ... 5⃣ Theano Python. ... 6⃣ Microsoft Cognitive Toolkit. ... 7⃣ PyTorch. ... 8⃣ Eclipse DeepLearning4J Link: https://dzone.com/articles/11-deep-learning-with-python-libraries-and-framewo You can put your question via @pythonethbot

Data Scientist A statistician? Kind of. Data science, in it’s simplest terms, can be described as a field of automated statistics in the form of models that aide in classifying and predicting outcomes. Here are the top skills that are required to be a data scientist: ✅Python or R ✅SQL ✅Jupyter Notebook Python — To expound on the skills above, most companies are looking for Python more than R. Some job descriptions list both; however, most people you are working with like the machine learning engineers, data engineers, and software engineers will not have familiarity with R. Therefore, to be a more holistic data scientist, Python will be more beneficial for you. SQL, at first, can seem more like a data analyst skill — it is, but it should still be a skill you employ for data science. Most datasets are not given to you in the business setting (as opposed to academia), and you will have to make your own — via SQL. Now, there are plenty of subtypes of SQL; like PostgreSQL, MySQL, Microsoft SQL Server T-SQL, and Oracle SQL. They are similar forms of the same querying language, hosted by different platforms. Because these are so similar, having any of these is useful and can be translated easily to a slightly different form of SQL. Jupyter Notebook could almost be the exact opposite of a machine learning engineer’s toolkit. A Jupyter Notebook is a data scientist’s playground for both coding and modeling. A research environment, if you will, allowing quick and easy Python coding that can incorporate commenting out of code, the code itself, and a platform to build and test models from useful libraries like sklearn, pandas, and numpy. Overall, a data scientist can be many things, but the main functions are to — meet with stakeholders to define the business problem — pull data (SQL) — EDA, feature engineering, model building, & prediction (Python and Jupyter Notebook) — depending on workplace, compile code to .py format and/or pickled model ✍... Will continue next time... You can put your comment or feedback @pythonethbot

Data Science Vs Machine Learning Introduction It seems as though even companies along with their job descriptions have some confusion on what constitutes a data scientist and machine learning engineer. At first, studying to become a data scientist. Data science is the researching, building, and interpretation of the model you have built, while machine learning is the production of that model.

Do you know the difference between Data Science vs Machine Learning? What are the main differences and similarities between data scientists and machine learning engineers? Anyone can explain? @pythonEthbot

Jupyter Notebooks Jupyter Notebooks are an extremely powerful tool for data analysis because they allow you to run python commands and see outputs within the structure of a notebook, which is helpful because in Data Analysis you are often running short commands to produce the data/visualizations you need for a certain investigation. To know how to install Jupyter notebook, looking at the following short video https://www.youtube.com/watch?v=5Yx6h7Mgiv0

ED-Mubarak to all Muslim Friends.

#EssayQuestion1 Why you want to learn python? Post your answer @pyDiscussion

COVID19 cases in East African Country.

It is a bar chart to compare two or more data. Countries in a high death rate.

The pie chart shows that 4.5 % of the total confirmed cases are died.

Writing a File using Python In the previous post we have seen that how to open and read a file using python script. Today, I have posting about how to write a file or create your own file using the script. Reading a file is all well and good, but what if we want to create a file of our own? With Python we can do just that. It turns out that our open() function that we’re using to open a file to read needs another argument to open a file to write to. script.py

with open('generated_file.txt', 'w') as gen_file:
  gen_file.write("I love python!")

Here we pass the argument 'w' to open() **in order to indicate to open the file in write-mode. The default argument is 'r' and passing 'r' to **open() opens the file in read-mode as we’ve been doing. This code creates a new file in the same folder as script.py and gives it the text What an incredible file!. It’s important to note that if there is already a file called generated_file.txt it will completely overwrite that file, erasing whatever its contents were before. #QuarantineYourself #LearnPython #LearnDataScience

Weapons of Math Destruction is a 2016 American book about the societal impact of algorithms, written by Cathy O'Neil. It explores how some big data algorithms are increasingly used in ways that reinforce preexisting inequality. @ETH_FDS

answer on the group @python4fds

#challenge1 level #very_easy Write a a script that asks a user to enter two integer and returns the addition of them #rule 1 your should handle an exeception if the user input is invalid #input_output_example #ex1 input a number: 5 input another number: 4 the sum is 9 #ex2 input a number: abc input another number: def your input is invaild #ex3 input a number: 1.2 input another number: 4.6 the sum is 5.8