Anaconda Data Science Certification

The Official Data Science Certification from the Creators of the World's Most Popular Python Data Science Platform

Objectively Demonstrate Your Data Science Expertise



Jumpstart your data science journey with a guided path



Prove your data science proficiency with Anaconda certification



Stand out from the competition and take control of your career path

As the creators of the most trusted Python data science platform, we understand the challenge of identifying truly qualified data scientists. Drawing from our own in-house experiences, our experts have distilled the principle components of a professional data science skill set into a carefully crafted, seven-topic Data Science Certification Path.

For each certification module, individuals will be administered a proctored, two-part exam. When you pass an exam, you will become Anaconda Certified (AC) in that area. Topic certifications may be completed in any order, and a topic area certification will be granted to successful candidates passing any single topic.

To earn the Anaconda Certified Professional (ACP) - Data Scientist designation, individuals must pass all seven module exams and complete a comprehensive exam.

The Data Science Certification Path

Data science certifications based on the Anaconda Data Science Certification Path will take your career to the next level.

  • AC - Anaconda Foundations

    Audience: Data Scientists, Data Analysts, Data Engineers, Software Devs, IT

    Individuals applying for the Anaconda Foundations Certification will need to demonstrate the ability to configure a simple computational environment with minimal computational resources. During the exam, you will:

    • Organize a local file-tree with command-line tools to enable convenient data access for analysis
    • Create, query, and manage conda environments from the command-line
    • Locate and install conda packages into environments from the command-line
    • Bundle conda packages (pure Python/R) at the command-line
    • Distribute a complete data analysis with all assets (notebooks, data, environments, etc.) using Anaconda Cloud
  • AC - Data Import & Export

    Audience: Data Analysts, Data Engineers, Data Scientists, Software Developers, IT

    Individuals applying for the Data Import & Export Certification will need to demonstrate the ability to access and manage data on a local machine or on remote computing resources. During the exam, you will:

    • Read from and write to local or remote SQL databases programmatically
    • Retrieve data programmatically from remote sources with standard protocols: HTTP, S3
    • Use programmatic APIs with standard formats for data persistence: CSV, JSON, HDF5, Excel
    • Use programmatic APIs for data serialization: pickle, shelve
    • Use programmatic APIs for data compression: zip, tar
    • Explain and contrast relative merits of standard techniques and APIs for data persistence, serialization, and compression
  • AC - Data Manipulations & Analysis

    Audience: Data Analysts, Data Engineers, Data Scientists

    Individuals applying for the Data Manipulation & Analysis Certification must demonstrate the ability to clean, organize, and analyze data. During the exam, you will:

    • Recognize standard data representations and data types required for a particular analysis: numeric values, text, dates and times, sequences/containers (e.g., sets, arrays, DataFrames)
    • Transform an unstructured data set into a structured form
    • Filter elements conditionally from a data set (e.g., remove null values, deduplicate, remove outliers)
    • Construct and apply prescribed transformations to structured data (e.g., modify timestamps to consistent time zone, bin by income brackets, correct spelling of categoricals, standardize representations)
    • Merge or join two or more structured data sets
    • Reshape tabular data sets for readability, usability, performance, or normalization
    • Group an existing data set according to one or more criteria
    • Encapsulate a working data analysis pipeline into reusable functions or scripts
  • AC - Data Visualization

    Audience: Data Analysts, Data Engineers, Data Scientists

    Individuals applying for the Data Visualization Certification must demonstrate the ability to visualize data and prepare it for presentation. During the exam, you will:

    • Use visualization for exploratory data analysis (e.g., to discover outliers and trends)
    • Choose a visualization that reveals relevant statistical properties of a data set
    • Build static graphics for publication
    • Build interactive graphics for web-based publishing
    • Encapsulate a working data visualization pipeline into reusable functions or scripts
  • AC - Statistical Analysis & Inference

    Audience: Data Scientists

    Individuals must demonstrate the analytical skills to interpret and understand volumes of data meaningfully. During the exam, you will:

    • Gather elementary summary statistics from a data set
    • Formulate a domain problem in a manner amenable to statistical analysis (e.g., identify random variables)
    • Perform quantitative and visual exploratory data analysis on a data set
    • Identify sources of error: data collection, overfitting, Type I errors
    • Construct, evaluate, and test a hypothesis (e.g., by an A/B test)
  • AC - Machine Learning

    Audience: Data Scientists

    Individuals are required to understand and apply common frameworks for machine learning. During the exam, you will:

    • Identify standard machine learning problems from a domain-specific context: classification, regression, clustering
    • Identify and implement pre-processing steps for standard ML algorithms (e.g., scaling, normalizing)
    • Apply multiple scoring metrics to evaluate model effectiveness
    • Construct a machine learning pipeline that includes data cleaning, data transforming, model building, and model optimization
    • Tune machine learning algorithm hyperparameters using a grid search
  • AC - Data Science at Scale

    Audience: Data Scientists

    Individuals applying for the Data Science at Scale Certification are required to apply all the skills described above in a big data context. During the exam, you will:

    • Distinguish big data analysis scenarios that legitimately require specialized hardware
    • Read data from and write data to distributed big data storage systems
    • Identify potential parallelism in computing workflows
    • Compute with out-of-core data sets
    • Process streaming, online, or high-frequency data
    • Deploy predictive ML models with big data


Anaconda Certified Professional (ACP) - Data Scientist (comprehensive)

The ACP - Data Scientist designation is the comprehensive, industry-standard certification for data scientists. To qualify, you must first complete the seven modules above. Once completion of the seven modules is verified, you are eligible to take the comprehensive examination, which will cover all the steps in the Data Science Certification Path. Passing the comprehensive exam will earn you the official title of Anaconda Certified Professional (ACP) - Data Scientist.

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