In this article, I will establish the various key elements and concepts of any data science project that would help you accurately understand the nuances of working in a machine learning / statistical framework. This summarized article is intended to help beginners in data science online course gather enough information and resources to juggle between their course targets, data science trends, business aspirations, and above all, acquire skill-based experiences from the leading data science project managers and trainers.
So, let’s start.
It’s impossible to miss the hundreds of problems that we think data science applications can solve for us. But, the hype is real when the practical applications actually take shape in the real life scenarios. That’s why the first step to starting data science project architecture should invariably start with “identification and introduction” to a PROBLEM you are hoping to solve using analytical and statistical models.
The key components therefore in any project would be listed as follows:
- Data Management System
- Programming architecture / Coding / Machine Learning Modeling
- Data Refinement
- Data Reporting, Visualization, and Presentation
- Validation and Quality Checks
- Final Approvals / Decision Making
Best Algorithms to Apply
I would suggest that choosing an algorithm for your data science project is entirely your own prerogative. I always use the Pareto Principle to meet my project demands which state that 20% of my data mining skills will meet 80% of the current problem. The rest 20% of the problem needs to be tackled through disciplined lifecycle management, Machine learning integration, and unique Open Source collaboration that puts focus on exceptional data modeling, evaluation, and business understanding, also referred to as data science online course for Business Intelligence and Analytics. Companies like Tableau, Sisense, and IBM, Google AI, and others focus on this aspect of business related data management for enterprise customers.
Here are the top algorithms or ML techniques that I figured out from my projects invariably work effectively in any given scenario.
- Time Series – Univariate and Multivariate
- Spatial Models
- Recommendations or Rating Systems
- Attribution Models
- Market Segmentation
- Predictive Intelligence and Scoring
- Supervised Classification
- Cognitive Control MODEL / Human memory simulation
- CNN and AI Neural Networking
- Random Forest
- Gradient Boosting / AdaBoost
There are countless others that can be applied but they need a massive volume of data and require precision handling that can only come with experience of working with highly advanced ML models and architectures.
Don’t worry, you will get there!
Experience With Data Management
Data is the single most critical factor that can decide the efficacy of your project. Any data source can be your database option. Typically, data analysts would identify databases like MongoDB, or AWS, or NoSQL before cleaning and aggregating them using probability based Exploratory Data Analysis or EDA. These are often fed into algorithms and models using advanced techniques such as Model Fitting, Blending, and Data Purging / cleansing.
If you are entrusted with the roles and responsibilities of data science project management, here are your key results areas:
You will work with a team of data analysts to create and maintain a database with the ability to manually fill or auto-fill fields and rows, based on the ability to create structure queries as part of the initial data modeling system management.
You are not a Database analyst / DBA – in a clear sense, you will be only handling a part of DBA and it would mostly be related to normalization and cleaning up scripts with the help of productivity management and AIOps tools. Thanks to rapid advancements in the Data management systems around AI ML tools, this has been completely outsourced to Cloud-based functions and applications, managed from the remote workplace and virtualized environment.
Security and Compliance
A key function of data project management is data governance and security compliance. At no stage of your operation should your database be exposed to internal and external threats arising from a data breach or phishing activities. In most cases, security frameworks alone expand the project timeline from 7 days to a productivity killer 90 days!
The biggest mistake a new data science analyst would make in their project management is shooting far from the target– all this happens because we only think of the problem as a standalone phenomenon, and not take into consideration the process, information architecture, security, and methodology.