[ad_1]
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_27.png)
Picture by Creator
Knowledge Science is a rising discipline. Let me help this with two completely different items of analysis.
The primary one is by Linkedin, carried out in 2022. The examine exhibits the fastest-growing job titles over the previous 5 years. The Machine Studying Engineer title is the fourth fastest-growing job. It’s a subbranch of Knowledge Science.
As an information scientist, it’s a must to construct your personal Machine Studying mannequin and put it into manufacturing. After all, you need to find out about net scraping, too, and all different levels of an information science challenge. Yow will discover all of it within the following components of the article.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_22.png)
Picture from LinkedIn
The second analysis comes from Glassdoor. It exhibits that knowledge science is within the high three of the 50 greatest jobs in America. Additionally, it’s been the identical for the final seven years. Let’s examine the statistics. 10,071 job openings and the 4.1/ 5 job satisfaction charge could be the explanation behind being top-of-the-line jobs. Additionally, the median base wage is $120,000 per yr.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_32.png)
Picture from Glassdoor
Now, it’s extra evident that knowledge science is a rising discipline. And likewise, the demand within the trade seems promising.
Because of this demand, the choices for studying Knowledge Science are growing. On-line programs and web sites are in style methods to be taught Knowledge Science ideas.
After mastering idea, doing actual initiatives will make you prepared for job interviews. It additionally helps enrich your portfolio.
But, the initiatives that junior builders are doing overlap typically. For example, home worth prediction, figuring out iris vegetation, or predicting the Titanic survivors. Despite the fact that they’re helpful initiatives, together with these in your portfolio may not be the perfect concept.
Why?
As a result of the recruiter has very restricted time to have a look at your CV. Right here is one other analysis that ought to make you extra conscious of this. It’s eye-tracking analysis carried out by ladders. This profession website exhibits that recruiters take a look at your resume for simply 6 seconds on common.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_33.png)
Picture from theladders
Now, I already talked about Knowledge Science’s recognition. Due to its recognition, many builders have already turned one step towards it. In order that makes the sphere extraordinarily aggressive.
To distinguish your self from others, you could do novel initiatives that can stand out. You will need to comply with sure levels to make the perfect of those initiatives. That method, you’ll stand out each by doing initiatives which can be completely different from others and by being thorough and systematic. In different phrases, you’ll entice the recruiter’s consideration after which preserve it by exhibiting them you recognize what you’re doing.
Right here’s the overview of the challenge levels.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_17.png)
Picture by Creator
Let’s now take a look at every. I’ll additionally offer you hyperlinks to completely different initiatives and coding libraries. No have to reinvent the wheel on this case.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_16.png)
Picture by Creator
Knowledge Assortment is the method of gathering knowledge from completely different sources. Over the past 10 years, the quantity of information created by completely different sources has been rising.
By 2025 the quantity of collected knowledge may have elevated to nearly thrice greater than in the present day.
Right here is the research carried out by IDC, exhibiting the adjustments in knowledge created by its sort.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_26.png)
Picture from import.io
It means the quantity of information collected will proceed to extend. That is the chance for companies and builders to scrape much more knowledge.
Why do you want extra knowledge?
After accumulating knowledge, the ultimate stage can be mannequin constructing and deploying it to manufacturing. So, growing this mannequin’s efficiency can be extremely necessary. A technique to do that is by accumulating extra knowledge. Right here the principle factor to give attention to is how your mannequin’s efficiency might be improved as the quantity of information will increase.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_24.jpg)
Picture from Deeplearning.AI
Now, many various methods exist to gather knowledge. Right here I’ll clarify 3 of them: studying from completely different file sources, net scraping, and API.
Accumulating Knowledge From Totally different File Sources
Many web sites and corporations will current you with knowledge in fine condition. But studying that in your coding setting will want a few traces of code.
For instance, this project goals to investigate the information of people that have enormous debt but have a tough time paying them off. As you’ll be able to see, this challenge additionally begins with studying knowledge through the use of the read_csv perform in pandas.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_12.png)
Now you might have knowledge, you’ll be able to carry out the exploratory knowledge evaluation this challenge is asking you.
For extra particulars on find out how to do it, here is the youtube video.
There are lots of completely different web sites the place you will discover open-source knowledge.
Listed below are 4 of them.
Discover the information there and browse them through the use of Python features as has been carried out above. In case your knowledge is available in different codecs (HTML, JSON, excel), you can even learn them utilizing Pandas functions.
Internet Scraping
Internet scraping is a means of utilizing automated instruments to scrape by means of the online and collect knowledge. This implies utilizing crawlers and scrapers. They map the web site HTML after which accumulate knowledge utilizing the given directions.
The Crawler
Internet crawlers search for any data to take from the web site. It helps you discover URLs that comprise the data you wish to scrape.
The Scraper
The scraper collects the data that you simply preset. Each scraper has a selector to find the data you wish to take from the web site.
How does it work?
Usually, they work collectively. First, the crawler takes the details about your matter and finds the URLs. Then the scraper locates the data you want through the use of the selector. Within the remaining step, you scrape this knowledge.
Knowledge Scraping Python Libraries
In Python, you’ll be able to scrape knowledge by working with completely different libraries. Listed below are 4 of them for you.
Right here is the video by Ken Jee. You possibly can see his Youtube sequence of Knowledge Science initiatives by dividing them into completely different steps. Within the second video, he explains the significance of information assortment. Then he scrapes knowledge from glassdoor through the use of Selenium to make knowledge evaluation of the information scientist’s wage.
API
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_4.png)
Picture by Creator
API stands for Software Programming Interface, which is used for communication between completely different applications. It helps two applications or functions to move data between them.
Youtube Scraper API
Here’s a Youtube API that can be used to collect knowledge within the subsequent video. It means that you can get channel statistics like the overall subscribers and think about counts, names of the movies, feedback, and extra.
On this video on The Vu Knowledge Analytics channel, the information is scraped from Youtube utilizing the Youtube API.
First, the information is processed. After that, there’s an exploratory knowledge evaluation, visualization, and extra.
You possibly can see some similarities to the earlier video, like cleansing knowledge and doing explanatory evaluation. That is regular as a result of most knowledge initiatives undergo the identical levels. However the knowledge you utilize in these levels and the way you utilize it is going to enhance your initiatives and enrich your portfolio.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_1.png)
Picture by Creator
It’s the part of turning your unstructured knowledge right into a significant model. The pipeline time period right here focuses on transformation. Typically this part can be known as knowledge cleansing, but visualizing pipelines and assigning them to the levels of information is smart.
After scraping, knowledge usually will not be in the perfect form.That’s why altering its format can be necessary. For example, if in case you have a small quantity of information however it comprises many NAs or missing values, filling these values into the common of the column will make it easier to to make use of this knowledge.
Or for instance, in the event you work with the date time column, the information sort of your column could be an object. That can forestall you from making use of the date time perform to that column. So you need to flip this column’s knowledge sort to date-time.
Having extra knowledge with higher high quality helps you construct a more practical mannequin.
Let’s give an instance of the transformation wanted in Machine Studying. In the event you construct a mannequin, your variables ought to be numerical. But some knowledge has categorical data. To show these variables into numerical, you need to do one hot encoding.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_2.png)
Picture by Creator
Realizing your knowledge is admittedly necessary when doing a challenge. To do this, you could first discover it through the use of completely different features.
Listed below are some widespread Pandas features utilized in knowledge exploration.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_8.png)
Picture by Creator
First look at your knowledge
head( ) perform provides you the possibility to have a look at the primary a number of rows of your knowledge. Additionally, the information( ) perform will provide you with a bit of details about your knowledge’s columns, similar to size and knowledge sorts.
describe( ) perform will give a abstract of descriptive statistics.
shape( ) perform will give details about the dimension of your knowledge, which outputs row/column as a tuple, respectively.
astype( ) perform lets you change the format of your columns.
Moreover, the possibility is you gained’t be working with a dataset, so merging knowledge can be a standard operation you’ll use.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_7.png)
Picture by Creator
Extracting significant data from knowledge turns into simpler in the event you visualize it. In Python, there are various libraries you should use to visualise your knowledge.
You need to use this stage to detect the outliers and correlated predictors. If undetected, they may lower your machine-learning mannequin efficiency.
Creating graphs makes this detection simpler.
Outlier Detection
The outliers typically occur on account of anomalies, and so they don’t assist your mannequin’s predictions. Detecting the outliers is adopted by eradicating them out of your knowledge.
Here we are able to see this distribution graph, which exhibits the utmost costs individuals are keen to pay for a room on Airbnb in Dublin.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_18.png)
Most individuals seek for a room beneath $200/night time, and particularly round $100/night time. But there are different individuals who seek for rooms between 500-600.
When constructing a mannequin, filtering it at a sure degree will make it easier to to foretell the habits extra precisely.
Correlation Graph
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_21.png)
Picture by Creator
Here, the correlation graph detects correlated predictors, which can lower your mannequin’s efficiency. You possibly can see the colour scale on the appropriate aspect of the graph, which exhibits that as the colour density will increase, the correlation will increase, too for each detrimental and optimistic.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_3.png)
Picture by Creator
Right here you’ll be able to see the completely different levels of mannequin constructing.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_9.png)
Picture by Creator
Selecting the Proper Algorithm
First step in mannequin constructing is selecting which algorithm sort to make use of. After all, it is dependent upon your matter.
Do you’re employed with numerical knowledge and plan to make predictions? Then your selection can be Regression. Or do you wish to classify the picture with a Classification algorithm? It’s as much as your challenge. For example, anomaly detection is commonly a well-liked challenge content material. It may be used for bank card fraud detection and makes use of the Clustering algorithms within the backend. After all, you should use Deep Learning in your challenge too.
Dataset Splitting
One method to consider your mannequin is to measure its efficiency on the information that the mannequin is unfamiliar with. Splitting knowledge into coaching and testing datasets helps you obtain that.
You practice your mannequin utilizing the coaching dataset after which check its efficiency utilizing the check set, which comprises knowledge the mannequin is unfamiliar with.
On this stage, in the event you already know which mannequin you’ll use, you’ll be able to skip utilizing the validation set. Nonetheless, if you wish to attempt completely different fashions to see which one is greatest, utilizing the validation set is for you.
A/B testing
After selecting the algorithm and splitting your dataset, it’s time to do A/B testing.
There are completely different algorithms. How will you make certain which one would be the greatest in your mannequin? One factor to test that is known as A/B testing in machine studying. A/B testing in Machine Studying implies that you’ll attempt completely different fashions to seek out the perfect in your challenge.
Strive all doable algorithms in your knowledge and discover the best-performing algorithm and proceed utilizing that one.
Efficiency Boosting Algorithms and Methods
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_23.png)
Picture by Creator
The efficiency of your mannequin typically might be enhanced through the use of completely different methods. Let’s give attention to three of them.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_5.png)
Picture by Creator
Dimensionality Discount
Dimensionality discount is used to seek out the predictors which can characterize your knowledge higher than others. This method makes your algorithm work sooner, and your mannequin predicts higher.
Here you’ll be able to see the appliance of the dimensionality discount approach within the sci-kit be taught library.
PCA
PCA stands for Principal Part Evaluation, which helps you establish the variety of predictors wanted to elucidate a sure proportion of your knowledge set.
Let me clarify this in an example, the anticipated time of supply is predicted. There are 100 predictors, so eliminating them will assist us to boost the velocity of the algorithm and outcome to have a greater working algorithm. That’s why the PCA algorithm is utilized.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_25.png)
PCA exhibits that no less than 60 consultant options are wanted to elucidate 80% of the dataset. Right here is the code within the sci-kit be taught library with an evidence.
Hyperparameters Tuning
When constructing a machine studying mannequin, there can be a whole lot of completely different parameters that can be utilized to make a prediction. Hyperparameters Tuning helps us discover the perfect of those parameters based on the wants of our challenge.
Grid Search
Grid Search lets you discover the perfect parameter values to optimize your machine studying mannequin.
Here is the code within the sci-kit be taught library, which explains what grid search is and provides you the code of implementation.
Preprocessing
It helps to scale your variables on the identical degree, that’s how your algorithm, which works for prediction, works sooner.
Commonplace Scaler
The principle concept is to vary your predictor’s imply, zero, and customary deviation to 1. Here is the sci-kit be taught library code that will help you calculate that.
Mannequin Analysis Metrics
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_19.png)
Picture by Creator
This step helps us to interpret the mannequin. That’s why there are completely different analysis metrics for various algorithms. The interpretation of algorithm outcomes will differ relying on the issue: to regression, classification, or clustering.
Regression
It’s a approach to seek out relations between numerical variables. Merely, we’ll use regression to foretell numeric variables.
Listed below are the 2 analysis metrics of regression issues.
MSE
Imply squared error is calculated by discovering the variations between the anticipated worth and precise worth and squaring the outcome, for each ingredient of your dataset.
Right here is the system.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_10.png)
Here is the implementation of MSE in sci-kit be taught with Python.
RMSE
RMSE is the foundation of the MSE.
Right here is the system.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_31.png)
Here is the implementation of RMSE in sci-kit be taught with Python.
Classification
Classification algorithms break up your knowledge into completely different teams and outline them accordingly.
The classification analysis course of typically features a confusion matrix, which incorporates True Class and predicted class. (True Optimistic, False Optimistic, True Unfavorable, False Unfavorable)
Right here is the implementation of the confusion matrix in sci-kit be taught.
Precision
That is to guage the accuracy of optimistic predictions.
Right here is the system.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_29.png)
TP = True Optimistic
FP = False Optimistic
Here is the implementation of precision in Python with the sci-kit be taught.
Recall
Recall or sensitivity, it’s the ratio of optimistic examples which can be appropriately categorized by your algorithm.
Right here is the system.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_13.png)
FN: False Unfavorable
Here is the implementation of recall in Python with the sci-kit be taught.
F1 Rating
It’s the harmonic imply of precision and recall.
Right here is the system
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_6.png)
Clustering
First step of clustering is grouping the information factors as a cluster. The second step is assigning them.
In the event you select the clustering algorithm, listed here are two analysis metrics to interpret your mannequin’s efficiency. Additionally, I added their system and the hyperlink to the sci-kit be taught library.
Purity
The purity is the proportion of whole knowledge factors categorized appropriately.
Right here is the system
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_28.png)
To calculate purity, you need to first calculate the confusion matrix.
RandIndex
It measures the similarity between two clusters.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_14.png)
TP = True Optimistic
FP = False Optimistic
TN = True Unfavorable
FN = False Unfavorable
Here is the implementation of RandIndex in Python with the sci-kit be taught.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_11.png)
Picture by Creator
After constructing your machine studying mannequin, it’s time to see its efficiency by presenting it to completely different customers utilizing cloud providers. Right here I’ll clarify how to do this through the use of two completely different libraries in Python: Flask and Django.
Additionally, there are completely different choices to run and host your mannequin, similar to Heroku, Digitalocean, pythonanywhere.com, which you should use in your personal challenge.
Internet APP with Flask
Flask is a Python library which will provide you with the possibility to put in writing net functions. For more information, please go to the official web site here.
Suppose you developed a machine learning model to foretell customers’ weight utilizing physique measurements and age.
To do this, you first have to entry the information so you’ll be able to construct A number of Linear Regression. Right here you’ll be able to attain the physique fats prediction open-source dataset to create a machine-learning mannequin which can predict the burden.
By utilizing pythonanywhere.com, you’ll be able to run your mannequin and host it on the web site on the identical time.
API with Django REST
I already talked about what API is, and now right here I’ll point out its implementation choices. You possibly can write your API through the use of the Django REST framework. Django is sort of a flask; it’s a micro net framework. By way of utilizing it, you can even code the backend and in addition frontend of the appliance and in addition write API.
Let’s discuss some great benefits of growing API with Django REST.
The group is energetic, and the documentation is in depth.
Additionally, here are the opposite Python frameworks; through the use of these, you’ll be able to develop an API.
On high of those six principal challenge levels, listed here are the bonus two.
Including Subscription to Your App
You possibly can embody a subscription plan in your Internet APP. Suppose you developed an OCR algorithm which targets to distract data from paperwork by doing picture processing, like Docsumo.
Listed below are the completely different choices of their pricing system.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_15.png)
Picture from docsumo.com
After all, earlier than turning your net utility right into a enterprise, many levels exist to comply with, but the ultimate vacation spot may very well be like this one.
To realize revenue through the use of API, here’s a website the place you’ll be able to add your API, and it additionally comprises hundreds of various APIs.
You possibly can monetize your API by including completely different subscription choices and acquire revenue afterward.
Here’s a face recognition api, which lets you flip your textual content into speech. This API probably makes use of CNN within the backend to acknowledge faces.
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_30.png)
Picture from RapidAPI
If you wish to develop an algorithm for face recognition, right here you’ll be able to attain an awesome tutorial written by Adem Geitgay, which explains the levels of face recognition with out going deeper and makes it simpler by growing his personal library.
Mannequin Upkeep
![From Data Collection to Model Deployment: 6 Stages of a Data Science Project](https://www.kdnuggets.com/wp-content/uploads/rosidi_data_collection_model_deployment_6_stages_data_science_project_20.png)
Picture by Creator
After importing your mannequin into manufacturing, it ought to be commonly maintained. Since your mannequin will use your consumer’s data, your algorithm ought to be up to date commonly.
Let’s say you construct a Deep Studying Mannequin which can predict the gap between two objects within the consumer’s photograph. When coaching your mannequin, you probably use high-quality photographs to do this. But, real-life knowledge may not be the standard you’re hoping for.
When your analysis metrics, you’ll be able to see the drop in your mannequin’s efficiency on account of this technical difficulty. After all, there are various doable options to beat this downside.
Considered one of them is including noise to the images in your mannequin.
That’s how your mannequin may also have the flexibility to foretell low-quality photographs too.
So to maintain your mannequin’s efficiency all the time excessive, it wants common updates like that. Additionally, you need to deal with your buyer’s suggestions commonly to maintain them glad along with your work and debug their issues.
Making a challenge typically begins with knowledge assortment. To distinguish from others, use some or all the information assortment choices I coated within the article. Together with that, use completely different open-source web sites the place you’ll be able to attain completely different knowledge units.
After accumulating your knowledge, constructing a pipeline, and turning your knowledge into the appropriate format comes subsequent. After that, it’s time to extract significant information from it by exploring it after which visualizing it. The following stage is to construct a mannequin.
Right here we examined mannequin constructing and efficiency booster algorithms, along with the analysis metrics.
After all, there are various choices for deploying your fashions into manufacturing, of which I coated some.
Following these levels, will make it easier to to ace your challenge, enrich your portfolio and earn doable revenue.
As you acquire expertise, be happy so as to add the stage based on your wants between them.
After you end the challenge, attempt mapping these levels into your thoughts. As a result of explaining them to the interviewers intimately, additionally will make it easier to to ace the interview and land a brand new job.
Nate Rosidi is an information scientist and in product technique. He is additionally an adjunct professor educating analytics, and is the founding father of StrataScratch, a platform serving to knowledge scientists put together for his or her interviews with actual interview questions from high firms. Join with him on Twitter: StrataScratch or LinkedIn.
[ad_2]
Source link