Category Archives: AI

What are the Languages and Frameworks commonly used in Data Analytics & Machine Learning

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Let’s explore the languages and frameworks commonly used in data analytics & Machine Learning:

For a short video presentation, click here.

Let’s start with the most commonly used language… Python.

PYTHON: Python is a popular programming language for data analytics. It has an intuitive syntax, a large number of resources, and extensive libraries for data analysis, visualization, and machine learning. Many data scientists and analysts prefer Python due to its versatility and robust ecosystem.
Some of the most popular libraries in Python that stand out for Data Analytics, Data Sciences and Machine Learning are:
NUMPY: Most commonly used as an open-source library for advanced mathematical analysis such as Arrays.
PANDAS: Most commonly used library for reading/writing data from SQL, CSV, Excel etc. Its useful and popular mostly for interacting with Big Data and large Databases.
SCIPY: This is particularly helpful in Machine Learning, Linear Algebra, Calculus such as Differentiation and Integration as well as Statistical Modeling.
MATPLOTLIB: This is most popularly used library for creating graphs, interactive Data Visualization and Grids. It works well along with Pandas, Scipy and NumPy
PLOTLY: This comes with API’s to build interactive and dynamic web-based data visualization.
SKI-KIT LEARN: This is especially used for Machine Learning algorithms, modeling and integrates well with Python Libraries such as NumPy, Pandas and Matplotlib.
BeautifulSoap: This library is specifically used for webscraping data from Websites, that can be further analyzed using Pandas and Numpy.

There are many more such libraries in Python that support Data Analytics and ML.

R: R is another widely used language for data analytics. It excels in data mining, statistical analysis, and exploratory data analysis. The R community provides strong support, making it a favorite among data professionals.

SQL: SQL (Structured Query Language) is crucial for querying data and managing databases. While not a traditional programming language, it plays a vital role in data analytics by allowing users to retrieve and manipulate data efficiently.

SCALA: Scala is a language that runs on the Java Virtual Machine (JVM). It’s commonly used in big data frameworks like Apache Spark, which enables distributed data processing and analytics.

JAVA: Java remains relevant in data engineering and analytics. It’s used for building scalable applications and integrating with big data tools like Hadoop and Spark.

Note that the choice of language depends on the specific task and context. Each language has its strengths, and data professionals often use a combination of these languages to tackle different aspects of data analytics. Additionally, frameworks like Apache Spark, Apache Flink, and Google Dataflow are essential for distributed data processing and analytics.

Images from Pixabay

Significance of Data Types in Data Analytics

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Let’s understand the practical significance of structured, unstructured, and semi-structured data in the realm of data analytics.

To view the short presentation video, click here.

Structured Data:
Structured data refers to information that is organized into a well-defined format. Structured data is typically stored in relational databases (RDBMS), where it follows a tabular structure with rows and columns. Each data element has a specific address, making it easy to analyze and query using standard SQL queries.
E.g., Relational SQL databases containing customer information, Online Transaction & Processing (OLTP), or inventory data, financial records, spreadsheets, and other well-organized datasets, sensors like GPS, Health Gadgets.

Significance:
Structured data is the backbone of traditional data analytics. It has organizational properties. It allows for efficient querying, reporting, and visualization. Machine learning algorithms often rely on structured data for training and prediction.

Semi-Structured Data:
Semi-structured data lies somewhere between structured and unstructured data. It has organizational properties to some extent. They do not strictly adhere to a fixed schema. They contain tags and metatags used to group data and organize it.
Examples of semi-structured data include Emails, XML files, JSON documents, Binary Executables, Integration of data from different sources. Semi-structured data is prevalent in scenarios where flexibility is essential. It’s commonly used for web data (HTML pages), log files, and social media posts. NoSQL databases (e.g., MongoDB) handle semi-structured data effectively.
Significance:
Semi-structured data allows for more dynamic data modeling. It accommodates varying data structures without sacrificing query efficiency. In data analytics, semi-structured data enriches insights by combining structured and unstructured elements.

Unstructured Data:
Unstructured data defies traditional formats and lacks a predefined schema. They do not have easily identifiable structure. They cannot be organized in relational DB in rows and columns, and does not follow any specific format, rules or even semantics. Unstructured data includes text, images, audio, video. Examples include social media posts, emails, and multimedia content in JPEG, GIG, PNG and documents such as PDF, PowerPoint presentations, Media logs. They have their own analysis tools for examining this type of data. Analyzing unstructured data requires advanced techniques. Natural language processing (NLP) and machine learning play a crucial role here. Tools like sentiment analysis, topic modeling, and image recognition are used.
Significance:
Unstructured data holds valuable insights often hidden within its chaos.
Sentiment analysis helps understand customer opinions.
Image recognition aids in medical diagnoses and security surveillance.

In summary, structured data offers order and simplicity, while semi-structured data provides flexibility. Unstructured data, though challenging, holds untapped insights waiting to be discovered with the right tools and techniques. As a data analyst, understanding the nuances of these data types empowers you to extract meaningful information and drive data-driven decisions.

The Role of Data Professionals

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The world of Data Analysis, AI & ML has four key professionals:

To view the short video presentation, click here.

DATA ENGINEER
KEY ACTIVITIES
DATA EXTRACTION, INTEGRATION & ORGANIZATION

KEY SKILLS
PROGRAMMING
DATABASE MANAGEMENT

ACTIONS
DEVELOP & MAINTAIN DATA ARCHITECTURE
MAKE USER DATA AVAILABLE
EXTRACT, INTEGRATE & ORGANIZE DATA
CLEAN, TRANSFORM & PREPARE DATA
DESIGN, STORAGE & MANAGE DATA IN THE DATA REPOSITORY
CONVERT DATA TO A USABLE FORMAT

DATA ANALYST
KEY ACTIVITIES
TRANSFORMS DATA INTO MEANINGFUL INFO TO ENABLE INSIGHTS & DECISION MAKING THROUGH STRONG ANALYTICS & STORY TELLING

KEY SKILLS
STATISTICAL MODELLING
SPREADSHEETS
QUERY BUILDING
DATA ANALYTICAL TOOLS FOR MODELLING (API’s)
PROGRAMMING INTERFACE
DESCRIPTIVE & DIAGNOSTIC ANALYTICS

ACTIONS
INSPECT & CLEAN DATA
IDENTIFY CORRELATIONS
FIND PATTERNS
APPLY STATISTICAL METHODS TO ANALYZE DATA
INTERPRETE AND PRESENT FINDINGS THROUGH MEANINGFUL SUMMARIES, CHARTS, TRENDS & STATISTICS

DATA SCIENTIST
KEY ACTIVITIES
PREDICTIVE ANALYTICS (POSSIBLE OUTCOMES IN THE FUTURE)
DATA MODELLING
DATA ANALYTICS TOOLS/ SKILLS, MATHEMATICS & STATISTICS

KEY SKILLS
MACHINE LEARNING
DEEP LEARNING
PREDICTIVE ANALYSIS
DATA ANALYSIS
MATHEMATICS & STATISTICS

ACTIONS
UNDERSTANDING THE DOMAIN
MACHINE LEARNING
DEEP LEARNING
PREDICTIVE MODELLING

BUSINESS/ BI ANALYSTS
KEY ACTIVITIES
BUSINESS INSIGHTS (INTERNAL OR EXTERNAL)
UNDERSTANDING OF DATA ANALYSTS & DATA SCINTISTS DELIVERABLES

KEY SKILLS
BUSINESS INTELLIGENCE
DOMAIN KNOWLEDGE

ACTIONS
DERIVE THE INSIGHTS FROM THE DATA ANALYSTS & DATA SCIENTISTS TO TAKE BUSINESS DECISIONS
PRESCRIPTIVE ANALYTICS (WHAT CAN BE DONE IN FUTURE BASED ON DESCRIPTIVE, DIAGNOSTIC & PREDICTIVE ANALYTICS)

Key Areas in the Data Ecosystem

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Key Areas in the Data Ecosystem. Free Image sourced from pixabay.

Click on this link for a visual presentation. For a detailed explanation, follow the blog below…

The Key areas where data ecosystem is currently useful are:

Financial Transaction Monitoring Systems & Financial Fraud Detection: These systems continuously track financial transactions for unauthorized activities or access to sensitive information. Detecting online fraud is of utmost importance in financial institutions. To identify patterns of financial transactions & fraud, one primarily relies on data mining techniques, artificial intelligence, and machine learning. Key players needed are:

  • Domain Experts: Subject matter experts who understand financial processes and can define rules and heuristics for detecting suspicious activities.
  • Data Scientists: who leverage machine learning algorithms to build predictive models that can identify fraudulent patterns.
  • Data Engineers: are responsible for data preparation, Data Repositories and creating scalable pipelines for real-time fraud detection.

Chat and Conversation in Recommended Engines: When it comes to chatbots and conversation, AI is used in most of the interactive sites to communicate and address to the end customers. The Natural Language Processing (NLP) Engines come into play that process and understand the user input, enabling chatbots to respond contextually.

  • Recommendation Systems: They suggest relevant responses based on historical interactions and user preferences.
  • Machine Learning Models: These models learn from user conversations to improve chatbot performance over time.

Data Mining:

  • Data mining extracts valuable insights from large datasets. Key components include:
  • Clustering Algorithms: Group similar data points together.
  • Association Rule Mining: Identifies patterns and relationships.
  • Classification Models: Predicts outcomes based on input features.
  • Anomaly Detection: Flags unusual data points.

Social Media Posts:

For effective social media strategies, the business or entities usually use one of these techniques for promotions or branding:

  • Social Media Analytics tools: These track engagement metrics, sentiment analysis, and audience demographics.
  • Content Scheduling Platforms: Automate posts and optimize timing.
  • Influencer Marketing Platforms: Connect with influencers for brand promotion.

Business Intelligence:

Business professionals gain a competitive edge by leveraging:

  • Data-Driven Decision-Making: Analyzing data to inform strategic choices.
  • Predictive Analytics: Forecasting market trends and customer behavior.
  • Personalization: Tailoring products and services to individual preferences.
  • Market Intelligence: Understanding competitors and industry dynamics.

In summary, the data ecosystem involves a synergy of technology, expertise, and analytics to drive business success. Whether it’s financial transactions, fraud detection, chatbots, social media, or Business strategy, data plays a pivotal role in shaping modern enterprises.

The Modern Data Ecosystem

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The Modern Data Ecosystem

Image sourced from pixabay.

As a Data Analyst, one is expected to have a good understanding of the modern data ecosystem and how it is consumed by various stakeholders. While there have been detailed and complex information and various thoughts around the data ecosystem, here is an attempt to simplify it a bit.

Watch the short video presentation from this link.

A data ecosystem is a platform that combines raw data from numerous providers and sources, transitioned and processed into a single Enterprise Data Repository and thereby building value through the usage of processed data.

It is a network of interconnected, independent, and continually evolving entities that includes data that has to be integrated from disparate sources such as images, videos, streaming data, user conversations, social media platforms, IoT devices, Real Time Systems and also legacy databases.

This raw data is organised, cleaned and further optimised in a single enterprise data repository. This allows the data to be analysed to generate insights, and finally collaborated with the active stakeholders to present and act on the insights obtained.

The modern data ecosystem has five archetypes that have emerged: data utilities, operations optimization and efficiency centres of excellence, end-to-end cross-sectorial platforms, marketplace platforms, and ecosystems that focus on the provision of data: exchange, availability, and analysis.

The consumption of data from the modern data ecosystem is done by various stakeholders such as Business entities, Products/Apps, Data Analysts, and Data scientists. Each of these stakeholders has a unique role to play in the consumption of data.

  • Business stakeholders use data to make informed decisions and drive growth. They use data to identify new opportunities, optimize processes, and improve customer experience.
  • Apps use data to understand user behaviour, preferences, and need and consume the data to provide personalized experiences to their end users.
  • Analysts use data to generate insights and provide recommendations to business stakeholders. They use data to identify trends, patterns, and anomalies.
  • Data scientists use data to build models and algorithms that can be used to make predictions and automate decision-making. They use data to train machine learning models, build predictive & prescriptive models, and develop algorithms.

To summarize, the modern data ecosystem is a complex network of interconnected entities that provides value through the usage of processed data. The consumption of data is done by various stakeholders such as Business, Apps, Analysts, and Data scientists. Each of these stakeholders has a unique role to play in the consumption of data, to make informed decisions, build models and algorithms, provide personalized experiences and generate insights.