- テストされたスキル
- 期間
- 110 分以内。
- 評価
- 自動
- テスト概要
-
選択問題
の知識を評価する。 Python 3.x, 論理的思考, シーケンス, ソフトスキル
プログラミング・タスク - レベル: ハード
Python | NumPy | Graph Convolutional Networks - シンプルなグラフ畳み込みネットワークを実装。
DevSkiller’s team produces Data Science interview questions for assisting recruiters aiming to hire Data scientists. Our tests are designed to ensure you find the perfect candidate through our unique range of challenges and questions.
We implement RealLifeTesting™ into our Data Science interview questions. This methodology is designed to simulate real-world scenarios and present candidates with realistic problems to solve. RealLifeTesting™ is a pioneering method for developer recruitment. Let us help you find your next Data scientist today, using our range of Data Science interview questions.
の知識を評価する。 Python 3.x, 論理的思考, シーケンス, ソフトスキル
Python | NumPy | Graph Convolutional Networks - シンプルなグラフ畳み込みネットワークを実装。
の知識を評価する。 Python 3.x
の知識を評価する。 Python 3.x
Python|ドラッグアナライザ - あなたはバイオテクノロジーのプログラミングチームのメンバーで、ラボの技術者向けに薬物分析を支援するシステムの作成を担当しています。あなたのゴールは、彼らが所見をシステムに入力し、意味のある分析を提供し、彼らが送信したデータの正しさを検証するアプリケーションを作成することです。
の知識を評価する。 ビッグデータ, パイスパーク, パイソン
の知識を評価する。 SQL
Python|PySpark|フリート管理企業 - スピード違反のイベントを検出し、既存の予測器の正しさを検証する。
の知識を評価する。 パイソン, スパーク
Python|PySpark|顧客嗜好モデル - マーケティングデータを前処理するためのデータエンジニアリングアプリケーションを実装する。
の知識を評価する。 ケラス, 機械学習, パイソン
Python|NLP、Keras|カスタマーレビューのセンチメント分析 - 映画や航空会社のカスタマーレビューのセンチメント分析とタグ付けを、マルチ出力のニューラルネットワークモデルを使って行う。
の知識を評価する。 機械学習, 強化学習
Python | PyTorch | 強化学習 | Deep Q-Network - DQNアルゴリズムの実装を完了する。
の知識を評価する。 パイソン
Python | PySpark | ML Logs Transformer - ログ変換パイプラインの実装を完了する。
SQL|切手カタログ|最高価格の3枚 - 最高価格の切手(価格と名前)を3枚選択します。
Python | Pandas | HTMLテーブルパーサー - HTMLテーブルをCSV形式のファイルに変換する関数を実装します。
の知識を評価する。 パイソン
Python | Pandas | HTMLテーブルパーサー - HTMLテーブルをCSV形式のファイルに変換する関数を実装します。
の知識を評価する。 機械学習, パイトーチ
Python | PyTorch, Computer Vision | Model Builder - Complete the implementation of a model training pipeline.
の知識を評価する。 パイソン
Python|車両販売レポート - 車両販売データウェアハウスに基づいてレポートを作成するアプリケーションを実装します。
の知識を評価する。 パイソン
Python | Pandas | A food delivery startup - Transform a database of orders by reducing its dimensionality and creating an additional analytical table.
の知識を評価する。 パイソン
Python | Client Base Creator - Implement the application to retrieve customer's contact data from the chat messages.
の知識を評価する。 機械学習, パイソン
Python | DNA Analyzer | Create and clean DNA strands - Implement 2 methods in Python that create and clean DNA strands.
の知識を評価する。 機械学習
Python | DNA Analyzer - Implement a method in Python that generates DNA statistical report.
assessing knowledge of *SQL
Python | NumPy | Aircraft measurement data processing - Complete data processing application that aggregates and compresses data streams using NumPy, Python and Data Analysis.
の知識を評価する。 SQL
Python | DNA Analyzer - Implement a method in Python that generates DNA statistical report.
の知識を評価する。 パイソン
Python | NumPy | Aircraft measurement data processing - Complete data processing application that aggregates and compresses data streams using NumPy, Python and Data Analysis.
の知識を評価する。 機械学習, パイソン
Python | DNA Analyzer | Create and clean DNA strands - Implement 2 methods in Python that create and clean DNA strands.
の知識を評価する。 パイソン
Python | DNA Analyzer - Implement a method in Python that generates DNA statistical report.
の知識を評価する。 機械学習, パイソン
Python Data Extraction, Processing - Complete and update the code for the program that extracts processes PDF files and converts them to a specific format for display/output.
の知識を評価する。 機械学習, アンドロイド
Android | Social Network login - Implement missing sections of LoginActivity and MainActivity, LoginManager and CredentialsStorage.
The driving force behind DevSkiller Data Science interview questions is the RealLifeTesting™ methodology. It powers our unique approach to developer testing. RealLifeTesting™ functions around the principle that to get the best out of a developer, you need to present them with challenges similar to their everyday work. We use RealLifeTesting™ to simulate a developer’s work environment and then set them realistic challenges to overcome. In this way, we are able to offer you a thorough overview of a developer candidate’s strengths and weaknesses from the initial screen stage of recruitment.
Say goodbye to endless hours of monotonous, in-house testing. At Devskiller, we can offer you a clear understanding of your applicants’ knowledge, coding ability, critical thinking, and time-management skills. Our testing method works remotely and efficiently, saving you hours of time and effort during the recruitment process.
Data Science is a way of making decisions and predictions through predictive causal analytics, as well as prescriptive analytics, and machine learning. A data scientist’s responsibilities include looking at exploratory data analysis, machine learning and advanced algorithms, and data product engineering.
DevSkiller’s Data Science interview questions can help you to whittle down the candidates who are the best critical thinkers. Data scientists need to possess the ability to objectively analyze the data presented to them before forming an opinion. The Data Science candidate you choose to recruit will need to show their proficiency in coding and be comfortable with a variety of programming tasks.
It will be preferable if your Data Science candidate is privy to various programming languages, but mainly Python and R. They will be analyzing data on a daily basis so they will need to demonstrate their proficiency in both mathematics and statistics.
Finally, if your candidate can demonstrate ability in machine learning, deep learning, or AI, then this will all work in their favor. Advances in these areas are happening rapidly so it will be advantageous if your Data scientist is up to date with advances in the industry, in order to remain ahead of the curve.
Some of our past clients have created their own interview questions, tailored to their business’s needs. Perhaps you would like to do the same?
Our range of Data Science coding tests can be altered to your needs. Opt for a test duration that suits you better, choose which questions are the most relevant, and even alter the difficulty level of each test.
Remote testing means you can conveniently assess candidates from all corners of the globe. Did we mention that you can even observe tests in real-time? That’s right, you can choose to observe how well each candidate is performing even while they are taking their test!
To improve candidate experience during our Python online tests, Devskiller has implemented a built-in Pycharm IDE directly into the browser. Our already warmed up, ready to use Pycharm IDE will reduce time during testing. This will increase the user experience for your candidates and help to reduce candidate drop-off during the hiring process.
Candidates no longer have to clone the code, wait for the dependencies to install or indexes to build. They can literally start coding as soon as they open the test invitation. This unique feature is just one of the innovations setting DevSkiller TalentScore apart from the competition when it comes to developer screening.
If you’re still not completely convinced by our Data Science coding tests, check out what others are saying about us:
“We’ve replaced a high-maintenance in-house solution with DevSkiller. Our process looks the same, however, the product gives us better performance. The results are also way easier to assess.
"DevSkillerのおかげで、すでにフィットしそうな応募者の貴重なオンサイト時間を節約することができました。応募者一人当たり3時間を節約しています。以前は応募者と技術的なタスクに費やしていた時間です。"
The RealLifeTestingTM methodology is behind all of DevSkiller’s Data Science interview questions and coding tests. We don’t use traditional game-like quizzes or algorithmic puzzles that don’t accurately assess how well a developer will actually perform in the role. Instead, we use RealLifeTestingTM to recreate a Data scientist’s everyday work environment and assess them using challenges that reflect those they usually encounter. RealLifeTestingTM provides us and our clients with a holistic view of each applicant’s entire skillset. When the challenges mirror real issues, then the responses reflect how well that candidate will cope.
Our Data Science interview questions expect candidates to possess the critical thinking needed to determine the best method for resolving problems they may encounter. Results are automatically generated and are assessed on the candidate’s decision-making and problem-solving skills.
One of the main advantages of DevSkiller testing is that our Data Science interview questions are easily accessible online. Recruiters can send test invites to their candidates and then the tests themselves can be taken from anywhere they choose. This is a great time-saver, as your Data Science candidates can send their tests back as soon as they’ve finished, no more waiting around for in-house tests to be completed.
Even better is that our tests are assessed automatically as well. Once the candidate has finished, our system gets to work on their answers and then produces an automated, non-technical report detailing how they performed. Meaning all the recruiter has to do is send out the invites and await the results.
The feedback we get from developer candidates is that they love how closely our tests resemble the real work they do. Developers often grow tired of developer testing involving algorithmic tests and tasks reciting coding patterns, as this method doesn’t allow them to really show off their skills. Once they realize our tests aren’t following the same pattern, they relish being given the chance to perform.
Our tests allow candidates to work on our state of the art in-browser IDE, or to use their own, and they can run unit tests, much like they would in their real work. Developers are awarded a chance to prove their actual software development skills and to use normal coding tools and conventions that reflect their work. It is refreshing for candidates to be able to prove their skills in a fair setting.