nTier offers analytics and data consulting that empowers organizations to transform, analyze, and take action with their data. No matter what industry you’re in—insurance, healthcare, finance, marketing, or logistics—our team of knowledgeable consultants offers tailored, cutting-edge solutions to maximize the potential of your data.
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DATA ANALYTICS
Our Analytics Services
Analytics and Visualization Solutions: Make the most of modern tools like Python and Tableau to improve your data analysis and visualization techniques.
Data Management Solutions: Improve the coordinated and secure management of your organization’s structured and/or unstructured data.
Business Strategy Solutions: Enhance the decision-making procedures within your firm and create a strategy to accomplish your desired future state vision.
Data Science Solutions: Gain a deeper understanding of your data using data science and machine learning techniques.
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Founded in 2006 on the core belief that there is a superior way to train technical teams
PYTHON
Click on the links below to view course outlines, pre-requisites, course length and course objectives.
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Python for Data Analytics
Course Number:
NTVPYT010
The Python for Data Analytics course is intended to develop data analysts capable of driving data-driven decisions in any organization. The course begins with a refresher on the Python programming language and builds on this knowledge to introduce students to the world of data analytics in Python. Students will learn to use advanced data structures in Python, access data from external sources, use descriptive analytics, perform statistical analysis, and visualize their data in meaningful ways.
Audience:
This course is appropriate for individuals and organizations seeking to learn the basics of data analytics in Python. Students are expected to begin the course with at least some programming knowledge. Pre-work can be provided to students with no programming experience.
Course Duration:
3 days
Prerequisites:
Little to some programming knowledge in any programming language, preferably Python.
Course Objectives:
Upon completion of this course, participants should:
- Write Python scripts that that take in user input and output results
- Use Anaconda to simplify package management
- Create and use advanced data structures like vectors, matrices, and data frames
- Access data from external sources and manipulate it with Python data structures
- Use descriptive analytics and statistical analysis techniques to derive useful insight from data
- Visualize and format data in common charts using matplotlib
- Drive data-driven decisions in your organization
Course Outline:
Part 1: Python Environment, Conventions, and Fundamentals Refresher
- What is Python?
- Brief history
- Uses
- Quirks of Python
- Introduction to Anaconda
- Writing Python Scripts
- Basic Data Types
- Functions
- Flow Control
- Decision-making Statements
- Looping Statements
- Python conventions
- Python best practices
Part 2: Introduction to Data Analytics
- The Data Analysis Process
- Descriptive Data Analytics
- Diagnostic Data Analytics
- Predictive Data Analytics
- Prescriptive Data Analytics
Part 3: Advanced Data Structures
- What are Data Structures?
- Why Do We Need Different Kinds of Data Structures?
- Lists
- Arrays / Vectors
- Matrices
- Factors (Categorical Data in pandas)
- pandas DataFrame
- CSV Files
Part 4: Accessing Data
- CSV Files in Python
- Reading CSV Files
- Parsing CSV Files
- Loading Data into Python Data Structures
- Outputting CSV Files
- Connecting to Databases in Python
- Reading Database Tables
- Accessing Data in Databases
- Loading Data into Python Data Structures
- Creating Data Tables
- Updating Data in Databases
Part 5: Data Wrangling
- Filter Out Bad or Irrelevant Data
- Derive Composite Fields
- Aggregating Data from Multiple Sources
- Standardization
- Normalization
Part 6: Introduction to Exploratory Data Analytics
- Explore the Data
- Answer the Question, “What Happened?”
- Identify Trends and Patterns
- Summary Statistics
- Clustering
- Pattern Tracking
- Regression Analysis
- Generate Primitive Reports
Part 7: Introduction to Statistical Analytics
- Statistical Analysis Basics
- Mean
- Median
- Mode
- Range
- Standard Deviation
- Linear Regression
- Hypothesis Testing
- Sample Size Determination
- How Do We Apply This Data to Business Decisions?
Part 8: Visualizing Data
- Charts
- Bar Charts
- Box Plots
- Histograms
- Line Plots
- Pie Charts
- Generate Business Reports
- Tableau, Power BI, and Other Tools
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Python for Data Analytics II
Course Number:
NTVPYT011
The Python for Data Analytics II course builds on concepts from the first Python for Data Analytics course. Students will learn to leverage Python generators to conserve memory, serialize data into multiple formats, collect data from the web, integrate with Excel, use regular expressions, and perform predictive analytics. In the end, students will obtain the skills necessary to use data to make projections capable of influencing a diverse array of business decisions.
Audience:
This course is intended for data analysts with some experience in Python and basic data analysis.
Course Duration:
3 days
Prerequisites:
The following are prerequisites for this course:
- Little to some programming knowledge in Python
- Python for Data Analytics course or understanding of the concepts
- Advanced Data Structures
- Accessing Data from External Sources
- Descriptive Analytics in Python
- Statistical Analysis in Python
- Data Visualization in Python
Course Objectives:
Upon completion of this course, participants should:
- Use Python generators
- Pickle data
- Serialize data to JSON, XML, CSV, bytes
- Collect data from the web
- Integrate your Python code with Microsoft Excel
- Write regular expressions for matching, searching, splitting, and replacing
- Apply predictive analysis techniques
- Develop models for predictive analysis
Course Outline:
Part 1: Python Generators
- The yield Keyword
- Generators
- Generator Expressions
- Generator Classes
- Generator Methods
- .send()
- .throw()
- .close()
Part 2: Python Data Serialization
- The Python pickle module
- JSON
- XML
- CSV
- Bytes
Part 3: Integrating Python with Web Data
- Sources
- FTP
- RESTful Data
- Screen Scraping
Part 4: Integrating Python with Excel
- Python Modules for Excel
- xlrd
- xlwr
- xlutil
- Interacting with Excel from Python
- Creating Excel Spreadsheets
- Reading Excel Spreadsheets
- Updating Excel Spreadsheets
Part 5: Regular Expressions in Python
- Regular Expression Syntax
- Special Characters
- Repetition Characters
- The re Module
- Regular Expression Objects
- re Functions
- Searching
- Matching
- Replacing
- Splitting
Part 6: Introduction to Predictive Analytics
- Answering the Question, “What could happen in the future?”
- Case Studies of Business Applications of Predictive Analysis
- Future Cash Flow
- Investment Performance
- Staffing Needs
- Behavioral Marketing
- Manufacturing Malfunctions
- Ethical Concerns of Predictive Analysis
- Infamous and Illegal, Redlining
- Three Pillars of Data Analytics
- Needs
- Data and Technology
- Actions and Insights
Part 7: Predictive & Prescriptive Analytics Techniques
- Decision Trees
- Regression
- Single Linear Regression
- Multiple Regression
- Neural Networks
- Time Series Modeling
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Python for Data Science and Machine Learning
Course Number:
NTVPYT012
The Python for Data Science and Machine Learning course builds on concepts from the Python for Data Analytics courses. Students will learn to prepare data for machine learning, implement machine learning algorithms, train machine learning models, and use evaluation metrics on their machine learning models. In the end, students will obtain the skills necessary to utilize machine learning for predictive analytics.
Audience:
This course is intended for data analysts with some experience in Python and basic data analysis.
Course Duration:
3 days
Prerequisites:
The following are prerequisites for this course:
- Moderate programming knowledge in Python
- Python for Data Analytics course or understanding of the concepts
- Advanced Data Structures
- Descriptive Analytics in Python
- Statistical Analysis in Python
- Data Visualization in Python
Course Objectives:
Upon completion of this course, participants should:
- Prepare data for machine learning
- Implement machine learning algorithms
- Use SciKit-Learn, Tensorflow, and Keras for machine learning tasks
- Successfully train and fine-tune machine learning models
- Evaluate machine learning models
Course Outline:
Part 1: Introduction to Machine Learning
- Applications of Machine Learning
- Challenges of Machine Learning
- Big Data
- Python Libraries for Machine Learning
- Artificial Neurons
- Supervised vs. Unsupervised Machine Learning
Part 1: Supervised Machine Learning
- Classification and Regression
- Generalization, Overfitting, and Underfitting
- Model Complexity and Dataset Size
- Linear Models
- Naive Bayes Classifiers
- Decision Trees
- Neural Networks and Deep Learning
- Random Forests
Part 2: Unsupervised Machine Learning
- Challenges in Unsupervised Machine Learning
- Preprocessing
- Handling Missing Data
- Handling Categorical Data
- k-means Clustering and Data Mining
- Dimensionality Reduction
Part 4: Training Models
- Linear Regression
- Gradient Decent
- Polynomial Regression
- Logistic Regression
Part 3: Model Evaluation and Improvement
- Cross Evaluation
- Grid Searching
- Evaluation Metrics
- CSV
- Bytes
MACHINE LEARNING
Coming Soon!
TABLEAU
Click on the links below to view course outlines, pre-requisites, course length and course objectives.
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Tableau Prep *NEW*
Course Number:
N/A
This two-day hands-on course will teach you how to use Tableau Prep, Tableau’s data preparation, data cleansing, and ETL tool. In this course, you will learn how to get your data in the right format for optimal efficiency while in Tableau itself.
Course Duration:
2 Days
Prerequisites:
Prior experience using Tableau, joining data in Tableau, and working with calculations
Course Outline:
Introduction to the workspace
Data cleansing and preparation concepts, such as joins, unions, wildcard matches, fuzzy matching, and addressing nulls, irregularities and outliers
When to do your data preparation in Tableau Prep vs. the normal Tableau Desktop
Understand data sampling
Connecting to and configuring data
Exploring data
How to create workflows
Preferred data structures in Tableau
Cleaning data
Shaping data
Combining data
Joining and unioning data
Opening a data sample and creating an output file
Best practices for data preparation
Complex flows
Exporting to various Tableau formats
Validating results
At the end of the course, students will be able to overcome many of their thorniest data cleaning and preparation issues, enabling their Tableau analysis and visualization process to be smoother and easier. In some cases, it can replace or ease data prep challenges being solved in other ways currently, such as using SAS for ETL processes or waiting long periods of time for IT to get the data just so, or doing a lot of manual rework in Excel.
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Tableau Like a Pro
Course Number:
N/A
This class is designed for someone who has moderate experience with Tableau and is ready to take basic skills to an advanced level. This class will put more tools in your analytical tool box, such as more complex calculations, more powerful interactivity, more complex chart types, and more ways to slice and dice your data to mine for critical insights.
Course Duration:
2 Days
Prerequisites:
Successful completion of the Tableau Analyst course, or other introduction to Tableau course, or moderate exposure and use in the tool. Students in this class should already understand the basics of connecting to data and building charts and dashboards.
Course Outline:
Calculations and Interactivity (Day 1)
- Calculated Fields
- Aggregate Calculations & Ratios
- Logical Calculations
- If/Then
- IIF
- Case/When
- IfNull
- IsNull
- ZN
- Date Calculations
- Date
- DateAdd
- DateDiff
- DateParse
- Today()
- Now()
- MakeDate & MakeDateTime
- Parameters
- Pre-defined Lists for Faster Filtering
- Top N Filter
- Reference Line Parameter
- Swapping Dimensions or Measures in a View
- What If Analysis
- Using Actions to Create Interactive Dashboards
- Filter Actions
- Highlight Actions
- URL Actions
- Combining Parameters and Actions: On Demand Charts
- Advanced Chart Types (usually focus on what’s most relevant to your needs)
- Bar in Bar Chart
- Bulletgraphs
- Sparklines
- Slope Charts
- Building a Slope Chart with a Continuous Axis
- Building a Slope Chart with a Discrete Axis (i.e. Rankings)
- Pie Charts on Maps
- Control Charts
- Pareto Charts
- Gantt Charts and Dual Axis Gantt Charts
Advanced Analytics and Storytelling (Day 2)
- Advanced Formatting & Dashboard Best Practices
- Layout Containers
- Floating Elements
- When to Use Which
- Effective Dashboard Layouts
- Layout Best Practices
- Titles and Labeling
- Color Choices
- Do
- Don’t
- Guided Analytics
- Advanced Tooltips
- Custom Shapes & Custom Color Palettes
- Advanced Segmentation
- Dynamic Sets
- Top N Sets
- Conditional Sets
- Combined Sets
- Clustering
- Advanced Analytics
- Reference Bands and Reference Distributions Explained
- Reference bands
- Reference Distributions
- 60, 80% of Total, Percentiles
- Quantiles
- Standard Deviation
- Box Plots
- Forecasting
- Reference Bands and Reference Distributions Explained
- Storypoints
- To Publish a Workbook to Tableau Server
Bonus Material (if there is time or if specifically requested)
- Level of Detail Calculations
- Advanced Extracts
- Using Custom SQL
- Performance Tuning
- Some Performance Best Practices
- Using Tableau Server
- A New Perspective on Using Server
End of Day 4 could be workshop time to answer questions, help them create some dashboards off their data, etc.
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Tableau Analyst Ninja *NEW*
Course Number:
N/A
This intensive 3-4 day bootcamp is designed to turbocharge your Tableau skills and take your analytical ability and the insights you provide to the next level. This assumes you’re already very familiar with the Tableau interface and want to go beyond bar charts and learn a variety of advanced charts, when to use them and how they help you be a better data analyst. This course will also teach you how to turn your formatting into magazine-quality infographics, as well as quantify impacts.
Course Duration:
4 Days
Prerequisites:
3-6 months of regular Tableau usage; familiarity with calculations and a desire to move fast
Course Outline:
How to understand what keeps the CEO up at night
How to ask the right questions
Where to find answers to these questions
What makes a visualization effective
The Ninja Analyst Framework
Advanced Time Series Analytics
- Line Chart with Avg. Lines
- Cycle Plots
- Line Chart with Year over Year Growth
- Line Chart with Percent Change and Difference
- Line Chart with Trend Line
- Running Total Charts
- Line Chart with Future Projection via Regression
- Forecasting
- How to use them and when
- How to interpret them
- How to adjust them
- Using the datediff calc to show differences in time periods
- Control Charts
- Small Multiples
Distributions
- Percent of Total and Double Labels
- Bar Chart with Max Color Calc
- Adding Ranks as a Dimension
- Highlight Tables
- Histograms
- Pareto Charts
Correlation Analytics and Relationships Between Data
- Scatter Plots
- Jitter Plots
- Quadrant Charts
- Actual vs. Budget or Actual vs. Target Bulletgraphics
- Ratios
- “Relative To” Charts
- Linear Regression
- How to create them
- How to interpret them
- When to use them
- Simple Multiple Linear Regression
- Understanding and testing for Statistical Significance with the Student’s T-test or Chi Square
Outlier Analysis
- Averages
- What’s changed and by how much and how big is the impact
- Standard Deviations
- 95th Percentile
- Box Plots
- Sets
- Clustering
- When to use
- How to interpret
- How to adjust
- KPIs and BANS
- BANS
- Reference Line Comparisons
- KPI Indicators with YTD vs. Prev YTD (or similar types of time periods)
Advanced Geographic Analysis
- Custom maps
- Custom territories or regions
- Mapbox
- Background maps
Other Advanced Charts
- Barbell Charts
- Funnel Charts
- Marginal Histograms
Advanced Dashboards
- Layouts
- Formatting
- Fonts
- Colors
- Key Insights
- Providing Recommendations
- Quantifying Insights and Impacts of Recommendations
- Adding Concrete Metaphors to Drive Your Message Home
- Storypoints
Using these skills to advance your career and get promoted
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Tableau Analyst
Course Number:
N/A
This is for students who are new to Tableau or who have limited experience with the software. Students will learn the fundamentals of the tool and how to use all the basic functionality to visualize their data. Students will learn how to connect to various data sources, build a variety of basic charts, and combine those insights into a useable dashboard. Students will also about sharing and publishing options.
Course Duration:
2 Days
Prerequisites:
None
Course Outline:
Tableau Fundamentals (Day 1)
- Simple Data Connections and the Data Connection Interface
- Understanding the Main Tableau Interface
- Building Simple Visualizations
- Saving Options
- Understanding and Utilizing “Show Me”
- Dimensions vs. Measures and How They Affect a Viz
- What if We Wanted to Convert a Measure to a Dimension? How Would the Viz Change?
- Continuous vs. Discrete Variables
- Basic Dates
- Setting the Fiscal Year
- Basic Aggregations
- Hierarchies
- Sorting
- Grouping
- Formatting
- Color
- Size
- Labels
- Detail
- Formatting Individual Elements vs. the Entire Sheet vs. the Entire Workbook
- Building Your First Dashboard
- Labeling, Annotations, Tooltips and Data Highlighting
- Axis Labels
- Annotations
- Tooltips
- Using the Data Highlighter
- Quick Filters
- Publishing and Sharing
Visual Best Practices and Visual Analytics (Day 2)
- Connecting to Different Databases and Data Formats
- Live vs. Extract Connections – The Basics
- Reshaping and Formatting Data for Tableau
- Managing Multiple Data Connections in a Single Workbook
- Cross Database Filters
- Cross Database Joins & Data Blending
- What about Data from 2 Different Sources?
- What’s the Difference Between Joins, Cross-Database Joins, and Blending?
- How Do I Create a Cross-Database Join?
- When Would I Use a Cross-Database Join vs. Data Blending?
- How Do I Blend Data?
- Understanding the Navigation Menu
- Showing Trends or Change Over Time
- Trends
- Rate of Change and Moving Average
- Part to Whole and Ranking Analysis
- The Standard Bar Chart
- The Stacked Bar Chart
- The Side-by-Side Bar
- Pie Charts
- Percent of Total and Running Sum
- Using the Index and Ranking Calculated Fields
- Creating Dual Axis Charts and Combo Charts
- Using Measure Names and Measure Values to Build a Data Table
- Totals and Subtotals
- Using Maps and Spatial Analysis
- Map Zooming
- Showing Relationships between Numbers
- Scatter Plots
- Trend Lines and Statistical Features
- Reference Lines
- Distributions
- Heatmaps and Highlight Tables
- Histograms
- Motion Charts