Hari Thapliyaal

Dr. Hari Thapliyal is a seasoned professional and prolific blogger with a multifaceted background that spans the realms of Data Science, Project Management, and Advait-Vedanta Philosophy. Holding a Doctorate in AI/NLP from SSBM (Geneva, Switzerland), Hari has earned Master’s degrees in Computers, Business Management, Data Science, and Economics, reflecting his dedication to continuous learning and a diverse skill set. With over three decades of experience in management and leadership, Hari has proven expertise in training, consulting, and coaching within the technology sector. His extensive 16+ years in all phases of software product development are complemented by a decade-long focus on course design, training, coaching, and consulting in Project Management. In the dynamic field of Data Science, Hari stands out with more than three years of hands-on experience in software development, training course development, training, and mentoring professionals. His areas of specialization include Data Science, AI, Computer Vision, NLP, complex machine learning algorithms, statistical modeling, pattern identification, and extraction of valuable insights. Hari’s professional journey showcases his diverse experience in planning and executing multiple types of projects. He excels in driving stakeholders to identify and resolve business problems, consistently delivering excellent results. Beyond the professional sphere, Hari finds solace in long meditation, often seeking secluded places or immersing himself in the embrace of nature.

Data Engineering

Data Engineering

Exploratory Data Analysis (EDA)

  1. Data sourcing – public and private data
  2. Data cleaning – handling missing values, handling invalid data, filtering data, standardization, etc.
  3. Univariate analysis – unordered vs ordered categorical variables, quantitative variables & measures of central tendency
  4. Segmented univariate analysis – basis of segmentation, comparison of averages and other metrics
  5. Bivariate analysis – bivariate on quantitative and categorical variables, correlation
  6. Derived metrics – introduction, type-driven metrics, business-driven metrics, data-driven metrics
  7. Examples
    • Feature engineering and selection.
    • Analyzing bike sharing trends.
    • Analyzing movie reviews sentiment.
    • Customer segmentation and effective cross-selling.
    • Analyzing wine types and quality.
    • Analyzing music trends and recommendations.
    • Forecasting stock and commodity prices

Data Mining

  1. Introduction: Definitions, Activities, Process, Key challenges
  2. Understanding Data:  Data Types/ Attribute Types,  Statistical Descriptions of Data
  3. Measuring Data Similarity and Dissimilarity
  4. Data Pre-processing – Overview
  5. Association Rule Mining

Data Modelling

Data Cleaning

Data Visualization & Interpretation

  1. Information overload and issues in decision making
  2. Design of visual encoding schemes to improve comprehension of data and their use in decision making
  3. Presentation and visualization of data for effective communication
  4. Elementary graphics programming, charts, graphs, animations, user interactivity, hierarchical layouts, and techniques for visualization of high dimensional data & discovered patterns

EDA Tools

  1. Pandas Profiling
  2. Lux