- Build an in-depth understanding of all the data concepts.
- Create your strong social media profile on LinkedIn and GitHub.
- Build 15+ projects including 5+ Major Projects.
- Showcase your skills with a portfolio of real projects.
- Work on Live projects in parallel to understand how companies create end-to-end software solutions and apply ML models to real-life problems.
Duration: 256 Hours of Learning (8 Months) and many more hours for practice and project building.
- Python
- Data Structures
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-Learn
- Statsmodels
- Natural Language Toolkit ( NLTK )
- PyTorch
- OpenCV
- Tableau
- Structure Query Language ( SQL )
- PySpark
- Azure Fundamentals
- Azure Data Factory
- Databricks
- 5 Major Projects
- Git and GitHub
I will prefer Python Programming Language. Python is the best for starting your programming journey. Here is the roadmap of python for logic building.
- Python basics, Variables, Operators, Conditional Statements
- List and Strings
- While Loop, Nested Loops, Loop Else
- For Loop, Break, and Continue statements
- Functions, Return Statement, Recursion
- Dictionary, Tuple, Set
- File Handling, Exception Handling
- Object-Oriented Programming
- Modules and Packages
Data Structure is the most important thing to learn not only for data scientists but for all the people working in computer science. With data structure, you get an internal understanding of the working of everything in software.
Understand these topics
- Types of Algorithm Analysis
- Asymptotic Notation, Big-O, Omega, Theta
- Stacks
- Queues
- Linked List
- Trees
- Graphs
- Sorting
- Searching
- Hashing
Python supports n-dimensional arrays with Numpy. For data in 2-dimensions, Pandas is the best library for analysis. You can use other tools but tools have drag-and-drop features and have limitations. Pandas can be customized as per the need as we can code depending upon the real-life problem.
- Vectors, Matrix
- Operations on Matrix
- Mean, Variance, and Standard Deviation
- Reshaping Arrays
- Transpose and Determinant of Matrix
- Diagonal Operations, Trace
- Add, Subtract, Multiply, Dot, and Cross Product.
- Series and DataFrames
- Slicing, Rows, and Columns
- Operations on DataFrame
- Different ways to create DataFrame
- Read, Write Operations with CSV files
- Handling Missing values, replace values, and Regular Expression
- GroupBy and Concatenation
- Graph Basics
- Format Strings in Plots
- Label Parameters, Legend
- Bar Chart, Pie Chart, Histogram, Scatter Plot
- Measure of Frequency and Central Tendency
- Measure of Dispersion
- Probability Distribution
- Gaussian Normal Distribution
- Skewness and Kurtosis
- Regression Analysis
- Continuous and Discrete Functions
- Goodness of Fit
- Normality Test
- ANOVA
- Homoscedasticity
- Linear and Non-Linear Relationship with Regression
- t-Test
- z-Test
- Hypothesis Testing
- Type I and Type II errors
- t-Test and its types
- One way ANOVA
- Two way ANOVA
- Chi-Square Test
- Implementation of continuous and categorical data
The best way to master machine learning algorithms is to work with the Scikit-Learn framework. Scikit-Learn contains predefined algorithms and you can work with them just by generating the object of the class. These are the algorithm you must know including the types of Supervised and Unsupervised Machine Learning:
- Linear Regression
- Logistic Regression
- Decision Tree
- Gradient Descent
- Random Forest
- Ridge and Lasso Regression
- Naive Bayes
- Support Vector Machine
- KMeans Clustering
- Measuring Accuracy
- Bias-Variance Trade-off
- Applying Regularization
- Elastic Net Regression
- Predictive Analytics
- Exploratory Data Analysis
If you are interested in working with Text, you should do some of the work an NLP Engineer do and understand the working of Language models.
- Sentiment analysis
- POS Tagging, Parsing,
- Text preprocessing
- Stemming and Lemmatization
- Sentiment classification using Naive Bayes
- TF-IDF, N-gram,
- Machine Translation, BLEU Score
- Text Generation, Summarization, ROUGE Score
- Language Modeling, Perplexity
- Building a text classifier
- Identifying the gender
To work on image and video analytics we can master computer vision. To work on computer vision we have to understand images.
- PyTorch Tensors
- Understanding Pretrained models like AlexNet, ImageNet, ResNet.
- Neural Networks
- Building a perceptron
- Building a single layer neural network
- Building a deep neural network
- Recurrent neural network for sequential data analysis
- Understanding the ConvNet topology
- Convolution layers
- Pooling layers
- Image Content Analysis
- Operating on images using OpenCV-Python
- Detecting edges
- Histogram equalization
- Detecting corners
- Detecting SIFT feature points
How to use it Visual Perception
- What is it, How it works, Why Tableau
- Connecting to Data
- Building charts
- Calculations
- Dashboards
- Sharing our work
- Advanced Charts, Calculated Fields, Calculated Aggregations
- Conditional Calculation, Parameterized Calculation
- Introduction to SQL: Learn the basics of SQL syntax, commands, and data types.
- Retrieving Data: Learn how to write queries to retrieve data from a database using SELECT statements, filtering, sorting, and grouping.
- Joins: Learn how to combine data from multiple tables using INNER JOIN, OUTER JOIN, and other types of joins.
- Aggregating Data: Learn how to use aggregate functions like SUM, COUNT, AVG, and MAX to summarize data.
- Subqueries: Learn how to use subqueries to retrieve data from one or more tables based on conditions.
- Creating Tables: Learn how to create tables, define columns, and set constraints.
- Modifying Data: Learn how to insert, update, and delete data in a table.
- Advanced SQL: Learn advanced SQL concepts such as transactions, views, stored procedures, and functions.
- Database Design: Learn about database design principles, normalization, and ER diagrams.
- Practice, Practice, Practice: Practice writing SQL queries on real-world datasets, and work on projects to apply your knowledge.
- What is BigData?
- How is BigData applied within Business?
- Resilient Distributed Datasets
- Schema
- Lambda Expressions
- Transformations
- Actions
- Duplicate Data
- Descriptive Analysis on Data
- Visualizations
- ML lib
- ML Packages
- Pipelines
- Packaging Spark Applications
- Foundation of Data Systems
- Data Models
- Storage
- Encoding
- Distributed Data
- Replication
- Partitioning
- Derived Data
- Batch Processing
- Stream Processing
- Microsoft Azure
- Azure Data Workloads
- Azure Data Factory
- Azure HDInsights
- Azure Databricks
- Azure Synapse Analytics
- Relational Database in Azure
- Non-relational Database in Azure
We follow project-based learning and we will work on all the projects in parallel.
- Understanding Git
- Commands and How to commit your first code?
- How to use GitHub?
- How to make your first open-source contribution?
- How to work with a team? - Part 1
- How to create your stunning GitHub profile?
- How to build your own viral repository?
- Building a personal landing page for your Portfolio for FREE
- How to grow followers on GitHub?
- How to work with a team? Part 2 - issues, milestone and projects
1️⃣ Awesome Public Datasets This list of a topic-centric public data sources in high quality.
2️⃣NLP Datasets Alphabetical list of free/public domain datasets with text data for use in NLP.
3️⃣Awesome Dataset Tools A curated list of awesome dataset tools.
4️⃣Awesome time series database A curated list of time series databases.
5️⃣Awesome-Cybersecurity-Datasets A curated list of amazingly awesome Cybersecurity datasets.
6️⃣Awesome Robotics Datasets Robotics Dataset Collections.
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For One-on-One sessions for Python, Data Science, Machine Learning, and Data Engineering.
Email your requirements Here: connect@himanshuramchandani.co