Data-Science

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• [ ] For Natural Language Processing (NLU = NLP + NLG) please check Natural Language Processing repository.

• [ ] For Machine Learning algorithms please check Machine Learning repository.

• [ ] For Deep Learning algorithms please check Deep Learning repository.

• [ ] For Computer Vision please check Computer Vision repository.

Data Science Tasks

Folders with all materials for specific task/domain

• AR VR
• Class Imbalance Problem
• Cloud Computing
  • AWS (Amazon Web Services)
• Data Analysis
• Data Analytics
• Data Engineering
• Data Preprocessing
• Data Processing
• Data Science Life Cycle Methodologies
• Data Warehouse
• Data-Centric AI
• Data
• Graph Neural Networks
• Machine Learning Ops (MLOps)
• Optimization
• Overfitting
• Pipelines
• SQL
• Statistics
• Tools and Tips

Educational Platforms

• OpenEDU

University courses 👩‍🎓

Julia language

Time Series

Online courses

• Специализация Наука о данных для руководителей
• Machine Learning Foundations

  Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science

GitHub Repositories :octocat:

Books

• Python Data Science Handbook
• Hands-On Machine Learning with Scikit-Learn and TensorFlow
  • Machine Learning Notebooks

    • A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

GitHub Repositories :octocat:

Tools

Papers

Certifications

• [ ] TensorFlow Developer Certificate
• [ ] Certified Analytics Professional (CAP)
• [ ] Cloudera Certified Associate: Data Analyst
• [ ] Cloudera Certified Professional: CCP Data Engineer
• [ ] Data Science Council of America (DASCA) Senior Data Scientist (SDS)
• [ ] Data Science Council of America (DASCA) Principal Data Scientist (PDS)
• [ ] Dell EMC Data Science Track
• [ ] Google Certified Professional Data Engineer
• [ ] Google Data and Machine Learning
• [ ] IBM Data Science Professional Certificate
• [ ] Microsoft MCSE: Data Management and Analytics
• [ ] Microsoft Certified Azure Data Scientist Associate
• [ ] Open Certified Data Scientist
• [ ] SAS Certified Advanced Analytics Professional
• [ ] SAS Certified Big Data Professional
• [ ] SAS Certified Data Scientist

Online Conferences, Meetups, Data Summer Schools

• Live Webinars & On-demand Recordings by ODSC COMMUNITY
• Data Science fwdays'19 (playlist)
• Webinars 2020, Computer Science UCU
• Eastern European Machine Learning Summer School, 2020 (Deep Learning and Reinforcement Learning
  • Program
  • Practical Sessions 2020, GitHub Repository

Twitter

• Christopher Manning
• Sebastian Ruder
• Prof. Sam Bowman

Podcasts

• Lex Fridman Podcast | Artificial Intelligence (AI)
• Machine Learning, Andrew Ng, Stanford
• Awesome Data Podcasts

Blogs

Companies Blogs

• • :octocat: Software Engineering Blogs

  • A curated list of engineering blogs

• Amazon | Science
• AWS Machine Learning Blog
• The Netflix Tech Blog
• Uber Engineering
• NVIDIA Developer

Other Blogs

• Towards AI
  • Tutorials

    • AI-related tutorials.

• Data Notes
• Louis Bouchard | @What's AI - Making AI Accessible
• Michael Galarnyk
• Data Science Dojo

Articles

• AI & ML дайджест от DOU
• Data Science и Machine Learning: с чего начать и где учиться

Communities

Telegram Chanels

• DataScience Digest
  • Collection of the top articles, videos, events, books and jobs on Machine Learning, Deep Learning, NLP, Computer Vision and other aspects of Data Science.

Main skills required by the data scientists vacancies

The research made by Faculty of Applied Sciences at UCU. Link on main article.

Big Data Software Engineer / Data Engineer

1. Linear algebra. Calculus. Statistics and Probability Theory.
2. Machine Learning Algorithms: regression, simulation, scenario analysis, modeling, clustering, decision trees, etc.
3. Python 3, Pandas, Scikit Learn, Keras, Tensor Flow, Numpy, PyTorch.
4. Data visualization.
5. Software engineering methodologies, functional programming or object-oriented programming.
6. DevOps: containerization and orchestration.
7. Classic DBs (relational or object): MySQL, PostgreSQL, RDS.
8. NoSQL (documented): MongoDB, Cassandra, HBase, Elasticsearch, Redis, DynamoDB.
9. NewSQL (hybrid/in memory): Memsql, VoltDB.
10. Query engines: Impala, Presto.
11. Cloud platforms (GCP, AWS). Cloud computation (Dataflow, Dataproc). Streaming (Pub/Sub, Kafka). Data storage (BigQuery, Cloud SQL, Cloud Spanner, Firestore, BigTable).
12. ETL Concepts / Processes.
13. Data Warehouse technologies, Data Lake architecture.
14. Data modeling: Bachman diagrams, Chen’s Notation, Object-relational mapping, etc.
15. Processing frameworks: Apache Spark (Pyspark/SparkR/sparklyr), Flink, Beam, Kafka streams
16. Data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.

Data Scientist

1. Python (PyCharm, Pandas, NumPy, bs4, sklearn, scipy). R.
2. Linear algebra. Calculus. Statistics.
3. Machine Learning techniques (Decision Trees, Random Forest, SVM, Bayesian, XG Boost, K-Nearest Neighbors) and concepts: regression and classification, clustering, feature selection, feature engineering, the curse of dimensionality, bias-variance tradeoff, SVMs.
4. Data visualization.
5. Data Mining (Clustering, Frequent Pattern Mining, Outliers Detection).
6. Neural Networks and ML Packages (sklearn/sqboost/Tensorflow/Keras, H20).
7. Cloud platforms (GCP, AWS). Cloud computation (Dataflow, Dataproc). Streaming (Pub/Sub, Kafka). Data storage (BigQuery, Cloud SQL, Cloud Spanner, Firestore, BigTable).
8. Databases: SQL and non-SQL, AWS cloud storage, GDPR data privacy.
9. Processing frameworks: Hadoop, Spark.
10. Business Intelligence Software (Power BI, Tableau, Qlik, Cognos Analytics).

Machine Learning Engineer

1. Computer science fundamentals, algorithms, mathematics, linear algebra, probability, and statistics.
2. Python (Pandas, Numpy, Scikit-Learn, Tensorflow, Keras).
3. Python visualization tools: matplotlib/seaborn, Plotly.
4. Machine Learning techniques (Decision Trees, Random Forest, SVM, Bayesian, XG Boost, K-Nearest Neighbors) and concepts: regression and classification, clustering, feature selection, feature engineering, the curse of dimensionality, bias-variance tradeoff, SVMs.
5. Deep Learning: Recurrent Neural Network (LSTM/GRU units), Convolutional Neural Network.
6. Machine learning frameworks (TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn) and ML techniques: GAN, ASR, RL.
7. Databases: SQL and non-SQL. Hadoop ecosystem.
8. Processing frameworks: Apache Spark (Pyspark/SparkR/sparklyr)
9. Cloud platforms (GCP, AWS).

Data Analyst

1. Math, Statistics (regression, properties of distributions, statistical tests, and proper usage, etc.) and Probability Theory.
2. Statistical programming software (R, Python, SAS, Matlab).
3. Predictive analytics (regression models, time-series analysis and forecasting, survival or duration analysis).
4. BI tools: Google Data Studio / Microsoft PowerBI / Tableau.
5. Classic DBs: MySQL.
6. MS Excel.
7. A/B testing.

NLP Engineer / NLP Data Scientist

1. Python (sklearn, nltk, gensim, spacy, Tensor Flow, PyTorch, Keras) and Python Data Science toolkit: Jupyter Notebook, Pandas, Numpy, Matplotlib/Seaborn, Scipy.
2. Databases: SQL and NoSQL (MySQL, MongoDB, PostgreSQL ) .
3. NLP libraries: NLTK, SpaCy, Stanford CoreNLP etc.
4. NLP techniques for text representation: (TF-IDF, Word2Vec), semantic extraction, data structures and modeling.
5. Methods of Information Extraction (NER, terminology extraction, keywords extraction, etc.)
6. Machine Learning techniques and concepts (regression, trees, SVM, ensembles) for NLP tasks.

CV Engineer

1. Linear Algebra. Geometry. Calculus. Statistics and Probability theory.
2. Python3, numpy, pandas, seaborn, scipy.
3. Computer vision / image processing libraries such as: OpenCV, Pillow.
4. Convolutional Neural Networks (LSTM, inception, residual, GAN).
5. Neural network frameworks: TensorFlow, PyTorch.
6. Computer vision algorithms and architectures: object detection, segmentation, face recognition, image processing, video processing.
7. Real-time CV systems based on Deep Learning.
8. Cloud model training (GCP, AWS), Cloud integration, Cloud Platforms.
9. Performance metrics in object detection and classification, such as mAP and related.
10. Big Data (Hadoop, Spark, Hive).

Deep Learning Engineer / Deep Learning Research Engineer

1. Python3: numpy, scikit-learn, pandas, scipy.
2. Statistics (regression, properties of distributions, statistical tests, and proper usage, etc.) and probability theory.
3. Deep learning frameworks: Tensorflow, PyTorch; MxNet, Caffe, Keras.
4. Deep learning architectures: VGG, ResNet, Inception, MobileNet.
5. Deepnets, hyperparameter optimization, visualization, interpretation.
6. Machine learning models.

The Data Science Interview Preparation

Typical interview construction

1. Software Engineering (for more visit Interview Preparation Repository)
2. Applied Statistics
3. Machine Learning
4. Data Wrangling, Manipulation and Visualisation

2. Applied Statistics

• Descriptive statistics (What distribution does my data follow, what are the modes of the distribution, the expectation, the variance)
• Probability theory (Given my data follows a Binomial distribution, what is the probability of observing 5 paying customers in 10 click-through events)
• Hypothesis testing (forming the basis of any question on A/B testing, T-tests, anova, chi-squared tests, etc).
• Regression (Is the relationship between my variables linear, what are potential sources of bias, what are the assumptions behind the ordinary least squares solution)
• Bayesian Inference (What are some advantages/disadvantages vs frequentist methods)

1. Introduction to Probability and Statistics, an open course on everything listed above including questions and an exam to help you test your knowledge.
2. Machine Learning: A Bayesian and Optimization Perspective by Sergios Theodoridis. This is more a machine learning text than a specific primer on applied statistics, but the linear algebra approaches outlined here really help drive home the key statistical concepts on regression.

• Sample Quant Exam