PHOTOGRAPHY MADE EASY
DS Direction
Photographic lighting is the illumination of scenes to be photographed. A photograph simply records patterns of light, colour, and shade; lighting is all-important in controlling
the image.
the image.
1
PM Direction
Project Manager вирішує завдання в обмежених умовах і коли потрібно - проявляє винахідливість та знаходить нестандартні рішення. Управляє бюджетом. Вибудовує коммунікацію всередині команди і з зовнішнім світом. Вміє планувати роботу, контролювати ресурси і дедлайни, оцінювати ризики, будувати відносини з командою і клієнтами
Назва посади
Що робить посада
Якому типу характеру підходить
Як виглядає типовий день
Рівні ЗП
Подальші кар'єрні перспективи
Hard Skills
Soft Skills
Вимоги в вакансіях спільні
Вимоги в вакансіях додаткові
По рівням вимоги Hard, Soft, в вакансіях
Big Data Software Engineer / Data Engineer
Linear algebra. Calculus. Statistics and Probability Theory.
Machine Learning Algorithms: regression, simulation, scenario analysis, modeling, clustering, decision trees, etc.
Python 3, Pandas, Scikit Learn, Keras, Tensor Flow, Numpy, PyTorch.
Data visualization.
Software engineering methodologies, functional programming or object-oriented programming.
DevOps: containerization and orchestration.
Classic DBs (relational or object): MySQL, PostgreSQL, RDS.
NoSQL (documented): MongoDB, Cassandra, HBase, Elasticsearch, Redis, DynamoDB.
NewSQL (hybrid/in memory): Memsql, VoltDB.
Query engines: Impala, Presto.
Cloud platforms (GCP, AWS). Cloud computation (Dataflow, Dataproc). Streaming (Pub/Sub, Kafka). Data storage (BigQuery, Cloud SQL, Cloud Spanner, Firestore, BigTable).
ETL Concepts / Processes.
Data Warehouse technologies, Data Lake architecture.
Data modeling: Bachman diagrams, Chen's Notation, Object-relational mapping, etc.
Processing frameworks: Apache Spark (Pyspark/SparkR/sparklyr), Flink, Beam, Kafka streams
Data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
Data Scientist
Python (PyCharm, Pandas, NumPy, bs4, sklearn, scipy). R.
Linear algebra. Calculus. Statistics.
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.
Data visualization.
Data Mining (Clustering, Frequent Pattern Mining, Outliers Detection).
Neural Networks and ML Packages (sklearn/sqboost/Tensorflow/Keras, H20).
Cloud platforms (GCP, AWS). Cloud computation (Dataflow, Dataproc). Streaming (Pub/Sub, Kafka). Data storage (BigQuery, Cloud SQL, Cloud Spanner, Firestore, BigTable).
Databases: SQL and non-SQL, AWS cloud storage, GDPR data privacy.
Processing frameworks: Hadoop, Spark.
Business Intelligence Software (Power BI, Tableau, Qlik, Cognos Analytics).
Machine Learning Engineer
Computer science fundamentals, algorithms, mathematics, linear algebra, probability, and statistics.
Python (Pandas, Numpy, Scikit-Learn, Tensorflow, Keras).
Python visualization tools: matplotlib/seaborn, Plotly.
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.
Deep Learning: Recurrent Neural Network (LSTM/GRU units), Convolutional Neural Network.
Machine learning frameworks (TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn) and ML techniques: GAN, ASR, RL.
Databases: SQL and non-SQL. Hadoop ecosystem.
Processing frameworks: Apache Spark (Pyspark/SparkR/sparklyr)
Cloud platforms (GCP, AWS).
Data Analyst
Math, Statistics (regression, properties of distributions, statistical tests, and proper usage, etc.) and Probability Theory.
Statistical programming software (R, Python, SAS, Matlab).
Predictive analytics (regression models, time-series analysis and forecasting, survival or duration analysis).
BI tools: Google Data Studio / Microsoft PowerBI / Tableau.
Classic DBs: MySQL.
MS Excel.
A/B testing.
NLP Engineer / NLP Data Scientist
Python (sklearn, nltk, gensim, spacy, Tensor Flow, PyTorch, Keras) and Python Data Science toolkit: Jupyter Notebook, Pandas, Numpy, Matplotlib/Seaborn, Scipy.
Databases: SQL and NoSQL (MySQL, MongoDB, PostgreSQL ) .
NLP libraries: NLTK, SpaCy, Stanford CoreNLP etc.
NLP techniques for text representation: (TF-IDF, Word2Vec), semantic extraction, data structures and modeling.
Methods of Information Extraction (NER, terminology extraction, keywords extraction, etc.)
Machine Learning techniques and concepts (regression, trees, SVM, ensembles) for NLP tasks.
CV Engineer
Linear Algebra. Geometry. Calculus. Statistics and Probability theory.
Python3, numpy, pandas, seaborn, scipy.
Computer vision / image processing libraries such as: OpenCV, Pillow.
Convolutional Neural Networks (LSTM, inception, residual, GAN).
Neural network frameworks: TensorFlow, PyTorch.
Computer vision algorithms and architectures: object detection, segmentation, face recognition, image processing, video processing.
Real-time CV systems based on Deep Learning.
Cloud model training (GCP, AWS), Cloud integration, Cloud Platforms.
Performance metrics in object detection and classification, such as mAP and related.
Big Data (Hadoop, Spark, Hive).
Deep Learning Engineer / Deep Learning Research Engineer
Python3: numpy, scikit-learn, pandas, scipy.
Statistics (regression, properties of distributions, statistical tests, and proper usage, etc.) and probability theory.
Deep learning frameworks: Tensorflow, PyTorch; MxNet, Caffe, Keras.
Deep learning architectures: VGG, ResNet, Inception, MobileNet.
Deepnets, hyperparameter optimization, visualization, interpretation.
Machine learning models.
Що робить посада
Якому типу характеру підходить
Як виглядає типовий день
Рівні ЗП
Подальші кар'єрні перспективи
Hard Skills
Soft Skills
Вимоги в вакансіях спільні
Вимоги в вакансіях додаткові
По рівням вимоги Hard, Soft, в вакансіях
Big Data Software Engineer / Data Engineer
Linear algebra. Calculus. Statistics and Probability Theory.
Machine Learning Algorithms: regression, simulation, scenario analysis, modeling, clustering, decision trees, etc.
Python 3, Pandas, Scikit Learn, Keras, Tensor Flow, Numpy, PyTorch.
Data visualization.
Software engineering methodologies, functional programming or object-oriented programming.
DevOps: containerization and orchestration.
Classic DBs (relational or object): MySQL, PostgreSQL, RDS.
NoSQL (documented): MongoDB, Cassandra, HBase, Elasticsearch, Redis, DynamoDB.
NewSQL (hybrid/in memory): Memsql, VoltDB.
Query engines: Impala, Presto.
Cloud platforms (GCP, AWS). Cloud computation (Dataflow, Dataproc). Streaming (Pub/Sub, Kafka). Data storage (BigQuery, Cloud SQL, Cloud Spanner, Firestore, BigTable).
ETL Concepts / Processes.
Data Warehouse technologies, Data Lake architecture.
Data modeling: Bachman diagrams, Chen's Notation, Object-relational mapping, etc.
Processing frameworks: Apache Spark (Pyspark/SparkR/sparklyr), Flink, Beam, Kafka streams
Data pipeline and workflow management tools: Azkaban, Luigi, Airflow, etc.
Data Scientist
Python (PyCharm, Pandas, NumPy, bs4, sklearn, scipy). R.
Linear algebra. Calculus. Statistics.
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.
Data visualization.
Data Mining (Clustering, Frequent Pattern Mining, Outliers Detection).
Neural Networks and ML Packages (sklearn/sqboost/Tensorflow/Keras, H20).
Cloud platforms (GCP, AWS). Cloud computation (Dataflow, Dataproc). Streaming (Pub/Sub, Kafka). Data storage (BigQuery, Cloud SQL, Cloud Spanner, Firestore, BigTable).
Databases: SQL and non-SQL, AWS cloud storage, GDPR data privacy.
Processing frameworks: Hadoop, Spark.
Business Intelligence Software (Power BI, Tableau, Qlik, Cognos Analytics).
Machine Learning Engineer
Computer science fundamentals, algorithms, mathematics, linear algebra, probability, and statistics.
Python (Pandas, Numpy, Scikit-Learn, Tensorflow, Keras).
Python visualization tools: matplotlib/seaborn, Plotly.
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.
Deep Learning: Recurrent Neural Network (LSTM/GRU units), Convolutional Neural Network.
Machine learning frameworks (TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn) and ML techniques: GAN, ASR, RL.
Databases: SQL and non-SQL. Hadoop ecosystem.
Processing frameworks: Apache Spark (Pyspark/SparkR/sparklyr)
Cloud platforms (GCP, AWS).
Data Analyst
Math, Statistics (regression, properties of distributions, statistical tests, and proper usage, etc.) and Probability Theory.
Statistical programming software (R, Python, SAS, Matlab).
Predictive analytics (regression models, time-series analysis and forecasting, survival or duration analysis).
BI tools: Google Data Studio / Microsoft PowerBI / Tableau.
Classic DBs: MySQL.
MS Excel.
A/B testing.
NLP Engineer / NLP Data Scientist
Python (sklearn, nltk, gensim, spacy, Tensor Flow, PyTorch, Keras) and Python Data Science toolkit: Jupyter Notebook, Pandas, Numpy, Matplotlib/Seaborn, Scipy.
Databases: SQL and NoSQL (MySQL, MongoDB, PostgreSQL ) .
NLP libraries: NLTK, SpaCy, Stanford CoreNLP etc.
NLP techniques for text representation: (TF-IDF, Word2Vec), semantic extraction, data structures and modeling.
Methods of Information Extraction (NER, terminology extraction, keywords extraction, etc.)
Machine Learning techniques and concepts (regression, trees, SVM, ensembles) for NLP tasks.
CV Engineer
Linear Algebra. Geometry. Calculus. Statistics and Probability theory.
Python3, numpy, pandas, seaborn, scipy.
Computer vision / image processing libraries such as: OpenCV, Pillow.
Convolutional Neural Networks (LSTM, inception, residual, GAN).
Neural network frameworks: TensorFlow, PyTorch.
Computer vision algorithms and architectures: object detection, segmentation, face recognition, image processing, video processing.
Real-time CV systems based on Deep Learning.
Cloud model training (GCP, AWS), Cloud integration, Cloud Platforms.
Performance metrics in object detection and classification, such as mAP and related.
Big Data (Hadoop, Spark, Hive).
Deep Learning Engineer / Deep Learning Research Engineer
Python3: numpy, scikit-learn, pandas, scipy.
Statistics (regression, properties of distributions, statistical tests, and proper usage, etc.) and probability theory.
Deep learning frameworks: Tensorflow, PyTorch; MxNet, Caffe, Keras.
Deep learning architectures: VGG, ResNet, Inception, MobileNet.
Deepnets, hyperparameter optimization, visualization, interpretation.
Machine learning models.
«The baseline for what seems "normal" in lighting is the direction and character of natural and artificial sources and the context provided by other clues»
Steve Jobs
Apple CEO
2
The Natural Light Baseline
To differentiate that role from that of "key" modeling when a modeling source moves behind the object it is typically called a "rim" or "accent" light. In portrait lighting it also called a "hair" light because it is used to create the appearance of physical separation between the subject's head and background.
When a photographer puts the sun behind an object its role in the lighting strategy changes from modeling the front of the object to one of defining its outline and creating the impression of physical separation and 3D space a frontally illuminated scene lacks.
Steve Jobs
Apple CEO
1818 Magazine by Stephanie Toole
To differentiate that role from that of "key" modeling when a modeling source moves behind the object it is typically called a "rim" or "accent" light. In portrait lighting it also called a "hair" light because it is used to create the appearance of physical separation between the subject's head and background.
3
Creating Natural Looking Artificial Lighting
A typical studio lighting configuration will consist of a fill source to control shadow tone, a single frontal key light to create the highlight modeling clues on the front of object facing the camera over the shadows the fill illuminates, one or more rim/accent lights to create separation between foreground and background, and one or more background lights to control the tone of the background and separation between it and the foreground.
There are two significant differences between natural lighting and artificial sources. One is the character of the fill and the other is more rapid fall-off in intensity. In nature skylight fill is omni-directional and usually brighter from above. That "wrap around" characteristic is difficult to duplicate with a directional artificial source.
