Data Scientist
Competency-based
ONET: 15-2099.00
2
Years
97
Skills
700h
Related instructions
Classroom instruction topics
- Statistics and Programming Foundation
- Data Science Foundation
- Data Engineering
- Data Modeling
- Model Deployment
- Big Data Foundation
On-the-job training
- Understand sampling, probability theory, and probability distributions
- Understand and apply different sampling techniques and ways to avoid bias
- Understand the concepts of probability, conditional probability, and the Bayes’ theorem
- Demonstrate knowledge of distributions such as the normal distribution and binomial distribution
- Demonstrate knowledge of descriptive statistical concepts
- Identify definitions of central tendency and dispersion knowledge of (mean, median, mode, standard deviations)
- Demonstrate knowledge about working with categorical data vs. numerical data
- Recognize the difference between descriptive and inferential statistics
- Demonstrate knowledge of inferential statistics
- Demonstrate understanding of the central limit theory and confidence intervals
- Demonstrate the ability to develop and test hypothesis
- Understand inference for comparing means (ANOVA)
- Understand inference for comparing proportions
- Articulate, and demonstrate knowledge of correlation and regression
- Understand how to test and validate assumptions for regression models
- Understand the impact of multicollinearity in regression
- Use a regression model to predict numeric values
- Demonstrate knowledge of python programming skills
- Demonstrate the ability to build python code using variables, relational operators, logical operators, loops, and functions
- Read and write data from csv and json files
- Use data structures such as lists, tuples, sets, and dictionaries
- Demonstrate knowledge of numpy and scipy libraries
- Learn to use Git repositories
- Demonstrate knowledge of anaconda, and jupyter notebooks
- Implement descriptive and inferential statistics using python
- Understand use of histograms and box plots to understand and visualize data distributions
- Master descriptive statistics python code calculating mean, median, mode, standard deviation, and percentiles; and identifying outliers
- Use python code to test hypothesis, calculate correlations and to predict a continuous variable using regression
- Validate regression assumptions
- Demonstrate ability to visualize data and extract insights
- Demonstrate expertise with python visualization libraries
- Demonstrate ability to visualize data for statistical analysis: histograms, box plots
- Demonstrate ability to visualize data for insight sharing with nontechnical users
- Demonstrate through a project the ability to analyze a dataset and communicate insights
- Demonstrate the ability to complete a project using all skills acquired up to this point: data exploration, descriptive and inferential statistics, and data visualizations
- Build a report with findings
- Deliver a presentation sharing insights
- Demonstrate solid communication skills (written and verbal)
- Demonstrate understanding of what is Data Science and what Data Scientists do
- Articulate what are the benefits of using data science
- Articulate what a data scientist does and the value of data scientists to an organization
- Understand some of the tools and the technology behind data science (IBM DSX and others)
- Articulate the value of data science in specific use cases
- Demonstrate ability to characterize a business problem
- Leverage business acumen to understand how to take a business problem and put it into quantifiable form
- Collaborate with cross-functional stakeholders to identify quantifiable improvement
- Define key business indicators and target improvement metric
- Demonstrate ability to formulate a business problem as a hypothesis question
- Formulate business problem as a research question with associated hypothesis
- Determine what data is needed to test the hypotheses
- Ensure hypotheses to be tested are aligned with business value
- Demonstrate use of methodologies in the execution of the analytics cycle
- Demonstrate how to apply the scientific method to business problems
- Demonstrate how to apply the CRISP-DM methodology
- Demonstrate understanding of an experimentation approach to insight finding and solution building
- Demonstrate through a project the ability to plan for the execution of a project
- Demonstrate through a project the ability to plan for the execution of a project
- Demonstrate the ability to setup a new project and follow the application of the scientific method and the CRISP-DM methodology.
- Build a report explaining the project plan
- Deliver a presentation sharing the project plan
- Demonstrate solid communication skills (written and verbal)
- Demonstrate ability to identify and collect data - multiple formats
- Demonstrate SQL skills for querying databases and joining tables
- Demonstrate ability to work with data from multiple data sources: SQL Data bases, NoSQL Databases
- Demonstrate ability to work with data in databases, csv and json files
- Demonstrate ability to manipulate, transform, and clean data
- Demonstrate an understanding of when/why data transformations are necessary
- Apply feature selection techniques
- Demonstrate understanding of techniques to clean data
- Demonstrate mastery of the pandas library for data transformation and manipulation
- Demonstrate expertise with slicing, indexing, sub-setting, and merging and joining datasets
- Demonstrate expertise with techniques to deal with missing values, outliers, unbalanced data, as well as data normalization
- Able to identify in which situations data may need to be scaled
- Able to select the best way to handle missing values
- Able to identify outliers and understand options to handle outliers
- Able to understand the impact of working with unbalanced data
- Able to construct a fully usable dataset
- Demonstrate through a project the ability to construct usable data sets
- Demonstrate the ability to complete a data engineering project using all skills acquired up to this point: cleaning and transforming data and building a usable dataset
- Build a report documenting decisions made on the data
- Deliver a presentation sharing process and results
- Demonstrate solid communication skills (written and verbal)
- Demonstrate understanding of Linear Algebra principles for Machine Learning
- Demonstrate understanding of working with vectors
- Demonstrate understanding of working with matrices
- Understand the application of eigenvectors and eigenvalues
- Demonstrate understanding of different modeling techniques
- Learn how to build models using libraries such as scikit- learn, and algorithms such as regressions, logistic regressions, decision trees, boosting, random forest, Support Vector Machines, association rules, classification, clustering, neural networks, ti
- Understand the process for experimentation and testing of different models on a dataset
- Demonstrate expertise selecting potential models to test, based on the available data, data distributions, and the goal of the project: explaining relationships or prediction
- Apply feature selection techniques
- Demonstrate use of Principal Component Analysis
- Demonstrate understanding of model validation and selection techniques
- Demonstrate successful application of model validation and selection methods
- Demonstrate use of cross-validation
- Demonstrate use of model accuracy metrics such as Confusion Matrix, Gain and Lift Chart, Kolmogorov Smirnov Chart, AUC ROC, Gini Coefficient, Concordant - Discordant Ratio, and Root Mean Squared Error
- Communicate results translating insight into business value
- Demonstrate the ability to turn data insight into business value
- Demonstrate the ability to adapt final deliverables and presentations based on the audience: data scientists, or business stakeholders
- Demonstrate through a project the ability to test different models on a dataset, validate and select the best model, and communicate results
- Demonstrate the ability to complete a project using all skills acquired up to this point: defining a business challenge as a hypothesis, selecting and evaluating different models on a date set and selecting a final “best” model
- Build a report with findings and conclusions for a data science audience and for a business audience
- Deliver a presentation sharing results for a data science audience and for a business audience
- Demonstrate solid communication skills (written and verbal)
- Deploy and monitor a validated model in an operational environment
- Demonstrate how to deploy a model
- Demonstrate the ability to monitor model performance and to define thresholds for model re-training
- Demonstrate how to use a deployed model from a python application
- Demonstrate through a project the ability to deploy and use a deployed model
- Demonstrate the ability to complete a small project building a simple application that will use a machine learning deployed model to predict results
- Understand the concept of Big Data, and how Big Data is used at organizations
- Understand what is Big Data and how Big Data is used at organizations
- Understand the concepts and major applications of Distributed and Cloud Computing paradigm
- Demonstrate knowledge of the Big Data ecosystems
- Understand the Big Data ecosystem and its major components
- Demonstrate knowledge of how each major component in the Big Data ecosystems works (HDFS, YARN, MapReduce, Spark, Pig, Hive, Flume, Flink, Kafka, etc.)
- Demonstrate hands-on experience with HDFS, MapReduce, Spark, Pig, Hive
- Demonstrate through a project expertise with Big Data platforms (Hadoop, Spark)
- Demonstrate the ability to complete a small project
- Participate as a data scientist on client engagements (internal or external)
- Participate as a data scientist in a minimum of 2 projects with clients (internal or external)
- Demonstrate team work abilities, and the ability to manage project risks, and stakeholder conflict
- Contribute to the profession by teaching or mentoring others
- Demonstrate commitment to the profession by writing publications, and teaching and mentoring others
- Demonstrate the ability to create reusable assets such as notebooks, libraries and documentation using the Hadoop and spark framework
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