Aseem Arora Python, Data Analysis, Machine Learning, Power BI
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Hi,
I am Aseem Arora, an experienced educator and IT professional. I specialize in teaching programming to students of all levels, from beginners to experts. My teaching style is straightforward and accessible, using simple language to explain complex concepts. I encourage students to ask questions repeatedly until they fully understand each topic. Every concept is accompanied by practical examples to reinforce learning. I prioritize logic building alongside programming skills, recognizing its importance in mastering the subject. Students can expect a comfortable learning environment, as I am dedicated to ensuring comprehension before moving on to new material.

Subjects

  • Node js Beginner-Expert

  • Advanced JavaScript Beginner-Expert

  • Python for Data Analysis Beginner-Expert

  • Power BI Dashboards Beginner-Expert

  • Excel & Research Data Analysis Beginner-Expert

  • Python for Data science, Machine Learning and Artificial Intelligence Beginner-Expert

Experience

  • Data Engineer (Aug, 2018–Present) at 14 years of experience in Data Analysis/Science
    Certainly! Here are 10 key points outlining my role as a Data Scientist with a focus on Power BI:

    Data Collection: Gather and integrate data from various sources to ensure a comprehensive dataset for analysis.

    Data Cleaning: Preprocess and clean the data to remove inaccuracies and prepare it for analysis.

    Data Analysis: Apply statistical methods and machine learning algorithms to analyze data and identify trends.

    Model Development: Build and validate predictive models to address business challenges and forecast future trends.

    Visualization Creation: Use Power BI to design and develop interactive dashboards and reports that effectively communicate insights.

    Data Interpretation: Translate complex data findings into actionable insights and recommendations for stakeholders.

    Collaboration: Work with cross-functional teams to understand business needs and tailor data solutions accordingly.

    Performance Monitoring: Track and assess the performance of models and visualizations, making adjustments as needed.

    Report Automation: Set up automated reporting systems in Power BI to streamline data updates and reporting processes.

    Continuous Learning: Stay updated with advancements in data science and Power BI to apply the latest techniques and tools.
  • Project Manager (Sep, 2011–Jul, 2018) at XNS Global

Education

  • MCA (Jun, 2007–Jun, 2010) from DSI, Bangalore

Fee details

    ₹200–500/hour (US$2.36–5.90/hour)

    Charges may differ based on putting efforts and time to complete the project/work/job.

Courses offered

  • Data Analysis Complete Course

    • ₹12000
    • Duration: 8 Weeks
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Hindi, Punjabi
    • Certificate provided: Yes
    Complete Data Analysis Course:
    This is beginner to advance level course. topics includes in this course are following:-
    1. Understanding of Data Analysis
    2. Introduction to statistics
    3. Data analysis with Excel
    4. Data Analysis with Python
    5. Introduction to SQL
    6. Building PowerBI dashboard
    7. Hands on practice project
    8. Assignments

  • Power BI crash course

    • ₹5000
    • Duration: 1 Week
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Hindi
    • Certificate provided: No
    Introduction
    Data Analysis with Power BI
    Power Query
    Star Schema Data Model
    Data & BI terms
    Power Pivot & DAX for Power BI
    Charts
    Tooltip Pages
    Data Analysis with Power BI - start to finish
    Data Analytics & Dashboard

  • Backend Development Complete Course

    • ₹15000
    • Duration: 50 Hours
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Hindi
    • Certificate provided: Yes
    Comprehensive backend development course with industry standard practices. Below is the details: -

    Module 1: Introduction to Node.js
    • Overview of Node.js
    • Setting up the development environment
    • Understanding the Node.js architecture
    • Basic Node.js commands and tools

    Module 2: Working with Node.js Modules
    • Built-in Node.js modules (e.g., fs, http)
    • Creating and exporting custom modules
    • Managing packages with npm
    • Using third-party packages

    Module 3: Asynchronous Programming
    • Callback functions
    • Promises and async/await
    • Event-driven programming and the event loop
    • Handling errors and exceptions

    Module 4: Building RESTful APIs
    • Designing RESTful APIs
    • Implementing CRUD operations (Create, Read, Update, Delete)
    • Handling HTTP requests and responses
    • Using middleware in Express.js

    Module 5: Using Express.js
    • Introduction to Express.js
    • Setting up an Express server
    • Routing and handling requests
    • Middleware and error handling

    Module 6: Database Integration
    • Connecting to databases (e.g., MongoDB)
    • Performing CRUD operations with database drivers
    • Managing database connections and transactions
    • Using Mongoose for MongoDB

    Module 7: Authentication and Authorization
    • Implementing user authentication (e.g., JWT, OAuth)
    • Managing user sessions and cookies
    • Securing API endpoints
    • Role-based access control

    Module 8: Deploying Node.js Applications
    • Preparing applications for production
    • Deploying to cloud platforms (e.g., Heroku, AWS)
    • Monitoring and maintaining deployed applications
    • Continuous integration and deployment (CI/CD)

    Module 9: Integrating Third-Party Libraries
    • Introduction to Cloud nary for image management
    • Using Multer for handling file uploads
    • Integrating other useful third-party libraries (e.g., Passport.js for authentication, Helmet for security)

    Module 10: Final Project
    • Building a complete backend application
    • Integrating all learned concepts
    • Testing and debugging the application
    • Presenting the project

  • Machine Learning

    • ₹15000
    • Duration: 12 Weeks
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Hindi
    • Certificate provided: Yes
    Detailed outline for a beginner-friendly machine learning course with python:

    Module 1: Introduction to Machine Learning
    • Overview of Machine Learning: History, definitions, and types of machine learning (supervised, unsupervised, and reinforcement learning).
    • Python Basics: Setting up the Python environment, basic syntax, python programming and libraries (NumPy, Pandas, Matplotlib, Scikit-Learn).
    Module 2: Data Preprocessing
    • Data Cleaning: Handling missing values, data imputation.
    • Data Transformation: Scaling, normalization, and encoding categorical variables.
    • Exploratory Data Analysis: Descriptive statistics, data visualization techniques.
    Module 3: Supervised Learning - Regression
    • Linear Regression: Understanding the concepts, implementation using Scikit-Learn.
    • Evaluation Metrics: Mean Squared Error (MSE), R-squared.
    • Hands-on Project: Predicting house prices using linear regression.
    Module 4: Supervised Learning - Classification
    • Logistic Regression: Understanding the concepts, implementation using Scikit-Learn.
    • Decision Trees: Fundamentals, entropy, and information gain.
    • Evaluation Metrics: Accuracy, precision, recall, F1-score, ROC curve.
    • Hands-on Project: Classifying emails as spam or not spam.
    Module 5: Unsupervised Learning
    • Clustering: K-Means clustering, hierarchical clustering.
    • Dimensionality Reduction: Principal Component Analysis (PCA).
    • Hands-on Project: Customer segmentation using K-Means clustering.
    Module 6: Model Selection and Evaluation
    • Cross-Validation: Techniques to evaluate model performance.
    • Hyperparameter Tuning: Grid search, random search.
    • Hands-on Project: Optimizing hyperparameters of a machine learning model.
    Module 7: Advanced Topics
    • Ensemble Methods: Bagging, boosting (Random Forest, Gradient Boosting).
    • Introduction to Neural Networks: Basics of neural networks and deep learning.
    • Hands-on Project: Implementing Random Forest and Gradient Boosting.

  • Deep learning with python

    • ₹15000
    • Duration: 12 Weeks
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Hindi, Punjabi
    • Certificate provided: Yes
    Detailed outline for a beginner-friendly deep learning course that includes Python:

    Module 1: Introduction to Deep Learning
    • Overview of Deep Learning: History, applications, and basic concepts.
    • Neural Networks: Introduction to artificial neurons, activation functions, and perceptron’s.
    • Python Basics: basic syntax, python programming and libraries (NumPy, Pandas).
    Module 2: Building Neural Networks
    • Forward Propagation: Understanding how data flows through a neural network.
    • Backpropagation: Learning how neural networks learn from data.
    • Loss Functions: Introduction to loss functions and their importance.
    Module 3: Training Neural Networks
    • Gradient Descent: Basics of optimization algorithms.
    • Regularization: Techniques to prevent overfitting.
    • Hyperparameter Tuning: Methods to optimize neural network performance.
    Module 4: Convolutional Neural Networks (CNNs)
    • Introduction to CNNs: Structure and applications.
    • Convolutional Layers: Understanding filters and feature maps.
    • Pooling Layers: Techniques for reducing dimensionality.
    Module 5: Recurrent Neural Networks (RNNs)
    • Introduction to RNNs: Structure and applications.
    • LSTM and GRU: Advanced RNN architectures.
    • Sequence Modelling: Applications in time series and natural language processing.
    Module 6: Advanced Topics
    • Transfer Learning: Leveraging pre-trained models.
    • Generative Models: Introduction to GANs and VAEs.
    • Deep Reinforcement Learning: Basics and applications.
    Module 7: Practical Projects
    • Project 1: Building a simple neural network for image classification.
    • Project 2: Implementing a CNN for a real-world dataset.
    • Project 3: Developing an RNN for text generation or sentiment analysis.

  • Natural language processing Complete Course

    • ₹15000
    • Duration: 12 Weeks
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Hindi, Punjabi
    • Certificate provided: Yes
    Module 1: Introduction to Python
    Part 1:
    ● Introduction and Installation of Jupyter Notebook/ Google Colab
    ● Introduction to data analysis
    Part 2:
    ● Performing arithmetic operations using Python
    ● Solving multi-step problems using variables
    ● Evaluating conditions using Python
    ● Combining conditions with logical operators
    ● Adding text styles using Markdown
    Part 3:
    ● Storing information using variables
    ● Primitive data types in Python: Integer, Float, Boolean, None and String
    ● Built-in data structures in Python: List, Tuple and Dictionary
    ● Methods and operators supported by built-in data types
    Part 4:
    ● Branching with if, else and elif
    ● Nested conditions and if expressions
    ● Iteration with while loops
    ● Iterating over containers with for loops
    ● Nested loops, break and continue statements
    Module 2: More Interaction with Python
    Part 1:
    ● Creating and using functions in Python
    ● Local variables, return values, and optional arguments

    ● Reusing functions and using Python library functions
    ● Exception handling using try-except blocks
    ● Documenting functions using docstrings

    Part 2:
    ● Interacting with the filesystem using the os module
    ● Downloading files from the internet using the urllib module
    ● Reading and processing data from text files
    ● Parsing data from CSV files into dictionaries & lists
    ● Writing formatted data back to text files
    Module 3: Numerical Computing with Numpy
    Part 1:
    ● Working with numerical data in Python
    ● Going from Python lists to Numpy arrays
    ● Multi-dimensional Numpy arrays and their benefits
    ● Array operations, broadcasting, indexing, and slicing
    ● Working with CSV data files using Numpy
    Part 2:
    ● NumPy Exercise
    Module 4: Analysing Tabular Data with Pandas
    Part 1:
    ● Reading a CSV file into a Pandas data frame
    ● Retrieving data from Pandas data frames
    ● Querying, sorting, and analysing data
    ● Merging, grouping, and aggregation of data
    ● Extracting useful information from dates
    ● Basic plotting using line and bar charts
    ● Writing data frames to CSV files

    Module 5 - Visualization with Matplotlib and Seaborn
    Part 1:
    ● Creating and customising line charts using Matplotlib
    ● Visualising relationships between two or more variables using scatter plots
    ● Studying distributions of variables using histograms & bar charts to
    ● Visualising two-dimensional data using heatmaps
    ● Displaying images using Matplotlib's plt.imshow
    ● Plotting multiple Matplotlib and Seaborn charts in a grid
    Part 2:
    ● Line graph
    ● Scatter plot
    ● Histogram and Frequency Distribution
    ● Heatmap
    ● Contour Plot
    ● Box Plot
    ● Bar Chart

    Module 6: Introduction to Text Processing
    ● Introduction to Text Processing: Understanding the basics of text
    processing and its applications.
    ● Handling Text in Python: Working with strings, reading and writing text
    files, and basic string operations.
    ● Regular Expressions: Using regex for pattern matching and text
    extraction.
    Module 7: Text Preprocessing
    ● Tokenization: Splitting text into tokens (words, sentences).
    ● Stop Words Removal: Identifying and removing common words that do
    not contribute much meaning.
    ● Stemming and Lemmatization: Reducing words to their root forms.
    ● Text Normalization: Converting text to a standard form (lowercasing,
    removing punctuation).

    Module 8: Text Representation
    ● Bag of Words (BoW): Representing text as a collection of words.
    ● TF-IDF (Term Frequency-Inverse Document Frequency): Weighting
    terms based on their importance.
    ● Word Embeddings: Understanding word2vec, GloVe, and other
    embedding techniques.
    Module 9: Basic Natural Language Processing (NLP)
    ● Text Classification: Categorizing text into predefined labels.
    ● Sentiment Analysis: Analyzing and predicting sentiments in text.
    ● Named Entity Recognition (NER): Identifying and classifying named
    entities in text.
    Module 10: Advanced NLP Techniques
    ● Topic Modeling: Identifying topics within a text corpus.
    ● Text Summarization: Generating concise summaries of text.
    ● Part-of-Speech Tagging: Identifying the grammatical parts of speech in
    text.
    Module 11: Libraries and Frameworks
    ● NLTK (Natural Language Toolkit): Overview and usage of NLTK for text
    processing.
    ● spaCy: Introduction to spaCy for advanced NLP tasks.
    ● Transformers: Using transformer models like BERT and GPT for NLP
    tasks.

    Module 12: Practical Applications
    ● Real-World Projects: Applying text processing techniques to real-world
    datasets and problems.

  • Advance Excel

    • ₹5000
    • Duration: 2 Weeks
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Hindi, Punjabi
    • Certificate provided: Yes
    Here's a detailed outline for an advanced Excel course designed for beginners:
    Module 1: Advanced Formulas and Functions
    • Complex Formulas: IF, AND, OR, VLOOKUP, HLOOKUP, INDEX, MATCH
    • Date and Time Functions: DATE, TIME, NETWORKDAYS, EDATE
    • Financial Functions: PMT, PPMT, IPMT, PV, FV
    Module 2: Data Analysis Tools
    • Pivot Tables: Creating and customizing pivot tables
    • Pivot Charts: Creating and customizing pivot charts
    • Data Analysis with Pivot Tables: Summarizing and analysing data
    Module 3: Conditional Formatting and Data Validation
    • Conditional Formatting: Highlighting cells based on criteria
    • Advanced Conditional Formatting: Using formulas for conditional formatting
    • Data Validation: Setting rules for data entry
    Module 4: Advanced Data Manipulation
    • Text Functions: CONCATENATE, LEFT, RIGHT, MID, LEN, TRIM
    • Array Formulas: SUMPRODUCT, FREQUENCY, COUNTIF, COUNTIFS
    • Data Cleaning: Removing duplicates, merging cells, and text-to-columns
    Module 5: Advanced Excel Features
    • Tables and Slicers: Creating and using Excel tables
    • Power Query: Importing and transforming data
    Module 6: Practical Projects
    • Project 1: Sales Analysis using Pivot Tables and Charts
    • Project 2: Budget Forecasting with Advanced Formulas

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