Is Udacity Predictive Analytics Nanodegree Worth Your Time And Money?

Predictive Analytics for business nanodegree review

The importance of data in business management cannot be overstated. Unfortunately, most business owners do not understand how interpret data and make informative business decisions. This is where data analytical tools come into play to provide comprehensive look into business practices and project future effects. Those who can turn business data into useful insights are the organization’s most precious assets, providing the scoop on how to make sensible business decisions that are both profitable and sustainable.

You can develop these skills by signing up to learn predictive analytics online from Udacity. This online academy is offering the Predictive Analytics for Business Nanodegree program. In this post, we will explain what predictive analytics is, how it works, and how businesses can leverage on it to gain competitive advantage. This post will end with a comprehensive review of the data analytics Nanodegree to help you decide if this online predictive analytics course is right for you.

So, what is predictive analytics?

In simple terms, predictive analytics refers to the use of data, statistical algorithms, and machine learning techniques to analyze historical data with the goal of identifying the likelihood of a future outcome.

How does predictive analytics work?

Predictive utilize known results to train or develop a model that can be used to predict values for different or unique set of data. Modeling produce results in the form of predictions that represent probabilities of target variables (such as revenue) based on the projected significance from the set of input variables.

There are two types of predictive models: classification models that predict class membership and regression models that predict numbers. The three most commonly used predictive modeling techniques include regression, decision tree, and neural networks.

Why predictive analytics is important for businesses

Businesses are turning to predictive analytics to help solve complex challenges and explore new opportunities. Some of the common applications of predictive analytics include:

Resolving crimes – by utilizing multiple analytics methods, you can improve the efficiency of detecting and preventing criminal behavior.   With the increasing advances in cybersecurity, high-performance behavioral analytics are capable of monitoring networks in real time to spot activities that may be indicative of fraud, zero-day vulnerabilities as well as advanced persistent threats.

Optimization of marketing campaigns – Predictive analytics can be used to analyze customer responses or purchasing patterns in order to come up with effective marketing campaigns that attract and retain customers.

Improving business operations –Most organizations are relying on predictive models to manage resources and forecast inventory. Airlines utilize predictive analytics to set ticket pricing while hotels and restaurants use predictive analytics to project the number of guests in any given day to maximize on occupancy and increase profits. Simply put, predictive analytics enables businesses to improve efficiency and revenue.

Managing risk – Financial institutions rely on credit scores (which are generated using predictive models) to assess borrowers’ likelihood of defaulting on their loans. The insurance industry also utilizes predictive analytics to assess claims and collections from their customers.

Why predictive analysis matters for an organization

  1. The Emergence of Big Data

Predictive analytics is often discussed in the context of big data. For instance, engineering data comes from instruments, sensors, and connected systems. An organization’s business system data may take the form of sales results, transaction data, customer feedback, and marketing information. Organizations are increasingly making data-driven decisions based on this valuable trove of information.

  1. Growing Competition

With growing competition, organizations seek an edge in bringing their products and services to the already crowded markets. Data-driven predictive models can help organizations find solutions to long-standing challenges in new ways. For instance, equipment manufactures can utilize predictive analytics to anticipate product failures, forecast energy needs, and keep operating costs down. A great application would be the use of sensors that measure vibrations in automotive parts to signal the need for maintenance before the car stalls on the road.

Business also uses predictive analytics to generate more accurate forecasts, like projecting the demand for electricity on the electrical grid. The forecasts enable effective resource planning.

  1. Cutting edge technologies for machine learning and big data

Organizations apply algorithms to large data sets using tools like Spark and Hadoop in order to make sense from big data. The data sources may include equipment log files, transactional databases, video, images, audio, sensors, and other types of data. Machine learning is applied to find patterns in data and build models that predict future outcomes. There are a range of machine learning algorithms available such as neural networks, linear and non-linear regression, support vector machines, decision trees, and other algorithms.

What you need to get started with Predictive Analytics

  1. The problem

The first thing you need to get started using predictive analytics is the problem to solve. What do you want to use past data to know about the future? What predictions do you want to make? It is equally important that you are clear about what you intend to do with the predictions. Clearly outline the decisions you will be driving with the generated insights as well as the actions you intend to take.

  1. The Data

Next, you will need data. And in today’s world, that means data from multiple sources. Data gathered by sensors, transactional systems, third-party information, web logs, call logs, and other data sources. You are going to need a data wrangler, or someone with data manager management skills to help you prepare the data for analysis. Preparing data for predictive modeling exercise also requires some understanding of both the data as well as the business problem. Defining your target is key to sound interpretation of the outcome. Data preparation tends to be one of the most tiresome and time consuming aspects of building predictive analysis models, so be prepared for it.

  1. Model building

After this, the process of building your predictive model begins. Thanks to availability of easy-to-use software, more and more people are able to build analytical models. However, you are still going to need a data analyst who will help you refine your models in order to come up with the best performer. Additionally, you will also need someone with IT skills to help you deploy your models. This means putting your models to work on your chosen data in order to generate your results.

  1. Team Work

Predictive Analytics require some sort of team approach. You need a team that understands the business problem to be solved. The team should know how to prep data for analysis. They should also be able to build and refine the models. The IT expert, for instance, will ensure that you have the right analytic s infrastructure for building and deploying models while the executive should be able to use the results of your analysis to make business decisions.

Udacity Predictive Analytics course Review: Is it worth your time and money?

Udacity is an online learning platform that was founded in 2012 by two Stanford professors with the goal of providing university quality education to learners from all over the world. Over the years, Udacity has partnered with top tech giants like Google, Amazon, and Tesla to create project-based courses in the tech and business fields. Udacity Nanodegree courses provide learners with in-depth knowledge on the subject topics to ensure that they are job ready within the shortest time period.

What you get from Udacity Nanodegree perks

Udacity Nanodegrees are online certifications that are designed to be completed in less than one year. Instead of coming with loads of unnecessary content, Nanodegree courses usually focus on building a strong knowledge base in a specific area of the field. Here are some of the extras that come with Udacity Nanodegree programs:

  1. Freedom

Once you sign up for your Nanodegree program, you will have the liberty to login and learn at your convenience, from any location. No more preset learning schedules or deadlines that interfere with your work or family life. You are the boss and in control of your learning schedule. All you need to keep in mind is your project deadlines.

  1. Community

To make learning fun and interesting, Udacity will grant you access to fellow learners with whom you can make connections and interact while learning.

  1. Mentorship

Learning alone can be a challenging experience. Udacity assigns each learner a mentor who will be around to answer any questions you might have, cheer you on, and ensure that your learning is as smooth as possible.

  1. Industry-focused projects

Udacity endeavors to keep their projects as close to real-world problems businesses encounter as possible. Most often, these projects are created in collaboration with top industry players. Each Nanodegree program comes with a series of problems to help reinforce learning. Most courses also come with a capstone project that you can add to your portfolio when looking for a job.

  1. Career preparation

Finally, Udacity’s Career Help experts will clean up your resume and get your LinkedIn profile ready to for the job market. They will also schedule a mock interview, complete with feedback to help you build confidence in readiness for your big day.

Udacity Predictive Analytics Nanodegree course instructors

Alongside Udacity’s amazing extras, the platform goes a notch higher to bring together a group of instructors worth paying attention to. For this online predictive analytics course, all the tutors have years of experience, some with over a decade in the industry. All the course’s tutors are staff at Alteryx Inc., which means that they are able to give you actual insights into actual practice that is used in today’s business analytics. Here are the tutors you will be learning from:

  1. Patrick Nussbaumer

Patrick has more than two decades of experience in data analysis world. He is currently the Director of Technical Activation at Alteryx Inc.,

  1. Ben Burkholder

With extensive data analysis knowledge, Ben works side by side with organizations to help them find solutions to their operational problems. He is also a senior solutions engineer at Alteryx Inc.,

  1. Maureen Wolfson

Maureen is another solution engineer in the team. She has over 2 decades of experience in customer geospatial analysis.

  1. Rod Light

Rod is a solution engineer who specializes in creating stories out of organizations’ data. He enjoys painting clear pictures with the acquired data to help businesses realize their goals and get a glimpse into their future.

  1. Tony Moses

Tony uses predictive analysis to help organizations make informed business decisions.

Udacity Predictive Analytics Nanodegree Course Prerequisites

This Nanodegree program is offered by Udacity’s School of Business and is meant to take you from the basics of predictive analytics and beyond. Having said that, you will need some experience in the following topics when signing up for this course:

  • Basic Math
  • Statistical Analysis
  • Windows
  • Spreadsheet creation

You do not need advanced knowledge in these topics. However, if you feel you need to brush up your skills, then you might want to check out Udacity’s free courses.

Predictive Analytics for Business Nanodegree Course Breakdown

Udacity predictive analytics for business Nanodegree course is broken down into 9 sections. During the course, you will be complete and submit a total of five projects. Here is the breakdown of the course’s modules:

  1. Introduction

This section includes a brief introduction into the Nanodegree as well as a mini regression modeling project.

  1. Problem solving with analytics

Here, you will be taken deeper into the analytical framework and building of linear regression modeling. You will then complete a more advanced project in regression modeling for deeper insights.

  1. Data Wrangling

In this section, you will learn about the different data types, common data issues, and how to format data and prepare datasets for analysis. You will be cleaning and formatting data to create datasets that you will later use for your predictive model.

  1. Classification models

In this section, you will learn how to build decision trees, logistics regression, forest and boosted models to score and predict results. You will end this module by building and applying a classification model.

  1. A/B Testing

In this section, you will be introduced to the fundamentals of A/B Testing, how to randomize design tests and matched pair design tests. You will be required to design and analyze an A/B Test using Alteryx A/B Testing and tool.

  1. Time Series Forecasting

Here, you will be introduced to the fundamentals of time series forecasting (it’s importance and uses). You will learn about the different models (ARIMA and ETS). You will use Tableau for analyzing and visualizing your data. You will then be required to complete a project based on what you have learned.

  1. Segmentation and Clustering

This is a much longer module where you will learn how to prepare data for clustering. You will also learn the basics of clustering models (K-Centroid). You will then learn how to visualize your data on Tableau. Your final project will be in 3 sections based on what you have learned in modules 4,7, and 8.

Bonus module

As a bonus, you will be introduced to Basic SQL, SQL Joins, SQL Aggregations, SLQ Sub queries and Temporary Tables, SQL Data Cleaning, Advanced SQL Window Functions, and Advanced SQL Joins and Performance Tuning.

Course Duration

Udacity recommends that you can complete this course within 3 months if you dedicate 10 hours per week to learning. That said, you are at liberty to set a learning schedule that works for you. Just keep in mind that taking much longer on the course can shoot up the cost of the Nanodegree as Udacity charges by month of access.

Course cost

Udacity offers two payment methods: per month of access or lump sum pay. Your payment option of choice depends on a number of things, one of which is the duration you think it will take you to complete the course.

  • Per month access price

The monthly price is $399. However, this could add up if you spend more time to complete the course. Therefore, before settling for this option, be sure to come up with a strategy that will help you get the most from the course within the shortest time possible.

  • Lump sum deal

The lump sum deal is calculated based on Udacity estimate, where they recommend that you can complete this course within 3 months. This option comes with a discount of up to 15% when you pay upfront.

What learners say about Udacity Predictive Analytics Nanodegree course

Udacity enjoys a solid reputation as a credible online learning platform. Overall, this course has a 4.7/5-star rating. Here is some of the feedback left behind by previous learners:

This is an overall very solid program if you want to get knowledge about basic predictive analytics tools. When I started, I did not even know what data analysis or predictive analytics mean, really. Now I have a good sense of variety of regression models, classification models, time series etc. which should definitely be a major contributor in my current goal of becoming a business analyst.” Emre H.

The first project was a great introduction to Predictive Analytics. This is a great place to begin to build upon foundational knowledge of analytics in order to solve problems in the business world on a granular level!Terence K. T.

A phenomenal and rewarding experience. Thank you for making it possible. This is definitely a unique offer to anyone wishing to experience of future learning. The course material was useful academically and professionally.Erkan M. 

A look at the job market for predictive analytics

There is no shortage of opportunities for predictive analysts. The demand for data scientists is on the rise with Forbes reporting that there will be a 28% growth in opportunities in this field heading into the next decade.

As far as income go, the entry level positions for data analysts come with a comfortable $60,000 salary package. This income can grow to an average of $124,000 per annum based on industry and years of experience.

Udacity Predictive Analytics Nanodegree review: Final thoughts

If you are a data scientist who is looking to take their analytical skill a notch higher, then this online predictive analytics course could be exactly what you need. You will gain the knowledge you need to help organizations make the predictions they need for informed business decisions. Throughout this course, you will gain knowledge followed by hands-on projects that mimic what you will face in the actual work environment.

Alongside the knowledge, you will also take full advantage of Udacity perks, some of which will definitely give you an edge in the job market. Now is the perfect time to jump in and start learning. Sign up for Udacity Predictive Analytics for Business Nanodegree today and join many successful graduates who are excelling in their industries courtesy of this course.

We wish you a happy learning!

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