Excel is a powerful spreadsheet software by Microsoft, facilitating data organization, analysis, and visualization. It features formulas, charts, and tables for efficient data management, making it indispensable for businesses.

• Introduction to Excel
• Fundamentals of Excel
• Data Exploration with In-Build Function
• Basic Visualisation Analytics
• Charts and Graphs

Advanced Excel goes beyond basic functions, offering intricate data analysis, complex formula usage, pivot tables, macros, and automation. It enables professionals to manipulate and interpret data with precision.

• Functions
• Look Up
• Vlookup, Index, Hlookup
• Date Functions and Time
• Data Tables and Validation
• Pivot Table
• Financial Functions
• Micros & Coding
• Custom Formatting and Advance Filters

Storytelling and Dashboard Creation involve crafting compelling narratives using data. By combining information into visually engaging dashboards, users can communicate insights effectively, aiding decision-making and enhancing data-driven storytelling for impactful presentations.

• Storytelling with Excel
• Basics of Dashboarding
• Creating Advance Dashboard

SQL (Structured Query Language) is a crucial tool in Data Science, allowing professionals to query, manipulate, and analyze large datasets stored in databases. It forms the foundation for extracting valuable insights and patterns to inform data-driven decision-making.

• Introduction to Databases & Queries
• Extracting Data using SQl
• Setting Up Big Queries
• Function Filtering & Sub-Queries
• Join Sub Queries
• Aggregation
• Date & Time Functions
• Indexing & Partitioning

Mathematics is the backbone of Data Science, encompassing statistics, linear algebra, and calculus. It provides the theoretical foundation for algorithms, modeling, and analysis, enabling the extraction of meaningful insights from complex datasets.

• Probability
• Bayes Theorem
• Distribution
• Discriptive Statistics & Outliers Treatment
• Hypothesis Testing & AB Testing
• ANOBA
• Correlation

Power BI is a business analytics tool by Microsoft that transforms raw data into insightful visuals and interactive reports. It connects to various data sources, facilitating data exploration, analysis, and sharing, empowering organizations to make informed decisions.

• Introduction Power BI
• Cleaning, Transforming, & Loading Data
• Designing a Data Model
• Visualisation
• Introduction to DEX
• Dashboarding
• Generating Reports
• Case Studies

Python programming is a versatile and high-level language known for its simplicity and readability. Widely used in data science, artificial intelligence, web development, and automation, Python is favored for its vast libraries and community support.

• Programming Foundation- Flowchart, Data Types & Operators
• Condition & Loops
• Functions
• Sting Handling
• Data Structures- List, Tuple, Dictionary, Set, Matrix
• OOPs

Data wrangling involves the cleaning, transforming, and organizing of raw, unstructured data into a usable format. It's a crucial step in the data preparation process, ensuring data quality for analysis and insights.

• Numpy
• Pandas
• Data Acquisition
• Web Scraping
• Beautifulsoup
• Selenium

Data visualization is the graphical representation of information and data. Utilizing charts, graphs, and maps, it simplifies complex datasets, making patterns and insights easily understandable for effective communication and decision-making.

• Matplotlib
• Seaborn
• Visualisation using Charts & Graphs

Machine Learning is a subset of artificial intelligence where algorithms enable systems to learn and improve from data. It involves training models to make predictions, identify patterns, and automate decision-making without explicit programming, fostering advancements in various fields.

• Regression
• Classification
• Desision Trees
• Random Forest
• Clustering
• DB SCAN
• EDA
• Feature Engineering
• Model Building
• Model Tuning

Big Data refers to vast and complex datasets that exceed the capacity of traditional data processing methods. It involves the collection, storage, and analysis of massive amounts of information to extract valuable insights, often requiring specialized technologies and tools for processing and management.

• Big Data Fundamentals
• Data Warehousing using Hive
• Apache Spark
• Big Data in the Cloud (AWS)
• NoSQL Databases

ML Ops, short for Machine Learning Operations, focuses on the operationalization and management of machine learning models throughout their lifecycle. It includes model deployment, monitoring, scaling, and collaboration between data scientists and IT operations to ensure efficient and reliable machine learning workflows in production environments.

• Flask
• Introduction to Docker & Containerisation
• ML Flow
• CI/CD
• AWS ML Operations

Deep Learning is a subset of machine learning that involves artificial neural networks with multiple layers (deep neural networks). It excels in learning intricate patterns and representations from vast datasets, proving highly effective in tasks like image and speech recognition, natural language processing, and autonomous systems.

• Nueral Networks
• Tensorflow
• Keras
• Forward and Backward Propagation
• Hyperparamenter Tuning
• Tensor Board

Computer Vision is a field of artificial intelligence that enables computers to interpret and understand visual information from the world, much like human vision. It involves tasks such as image recognition, object detection, and video analysis, contributing to applications like facial recognition, autonomous vehicles, and medical image analysis.

• Convolutional Nureal Networks
• Data Augmentation
• Hyperparamenter Tuning and Propogation
• CNN Architecture- Alex, VGG, Inception, etc
• Object Segmentation, Localisation & Ditection
• GANs

Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and human language. It involves the development of algorithms and models to enable machines to understand, interpret, and generate human language. NLP is crucial in applications such as chatbots, language translation, sentiment analysis, and text summarization.

• Text Preprocessing
• Regular Expressions
• Semantic Processing
• Introduction to Deep NLP
• Co-occurence Matrix
• Word Embedding
• Latent Semantic Analysis
• SkipGram & Word2Vec
• Sentment Analysis
• Case Studies

Preparing for technical interviews often involves a solid understanding of Data Structures and Algorithms. This includes topics like arrays, linked lists, stacks, queues, trees, graphs, sorting algorithms, searching algorithms, and dynamic programming. Proficiency in these concepts is crucial for solving coding problems commonly encountered in software engineering interviews. Regular practice, problem-solving on platforms like LeetCode or HackerRank, and understanding the time and space complexity of algorithms are essential for success in technical interviews.

Competitive programming is a form of intellectual sport where participants solve well-defined algorithmic and programming problems within a specified time frame. Engaging in competitive programming is an excellent way to prepare for technical interviews, as it hones problem-solving skills, algorithmic thinking, and coding efficiency. Practicing on platforms like Codeforces, LeetCode, or HackerRank helps individuals enhance their programming abilities and tackle algorithmic challenges commonly encountered in interviews.

Resume building involves creating a professional document summarizing an individual's education, work experience, skills, and accomplishments. A well-structured resume should be clear, concise, and tailored to the specific job or industry. It serves as a key tool for showcasing qualifications and attracting the attention of potential employers during the job application process.

A cover letter is a personalized document accompanying a job application, providing a brief overview of the applicant's qualifications, skills, and experiences. It explains why the candidate is suitable for the position, demonstrating enthusiasm and highlighting specific achievements. A well-crafted cover letter complements the resume and helps create a positive first impression on potential employers.

LinkedIn optimization involves refining your LinkedIn profile to enhance visibility, professionalism, and networking opportunities. This includes using a professional photo, crafting a compelling headline, writing a detailed and engaging summary, showcasing relevant skills and experiences, obtaining endorsements and recommendations, and actively engaging with connections and content on the platform. A well-optimized LinkedIn profile can significantly contribute to personal branding and professional opportunities.

GitHub optimization involves enhancing the efficiency, visibility, and collaborative aspects of a GitHub repository. This includes well-organized code structure, informative README files, clear documentation, meaningful commit messages, issue tracking, and leveraging features like branches and pull requests. Optimizing GitHub repositories fosters collaboration, code quality, and project management.

Effective communication skills are crucial in job interviews. This involves clear articulation, active listening, concise responses, and non-verbal cues. Demonstrating confidence, enthusiasm, and the ability to communicate ideas coherently enhances one's chances of making a positive impression during an interview.

Interview etiquette encompasses a set of behaviors and manners that individuals should exhibit during job interviews. This includes arriving on time, dressing appropriately, maintaining eye contact, being polite, expressing gratitude, actively listening, and sending a thank-you note afterward. These practices contribute to a positive impression and professional interaction during the hiring process.

An aptitude preparation kit is a comprehensive resource designed to enhance skills in areas such as numerical reasoning, logical thinking, and problem-solving. It includes practice tests, study materials, and tips to help individuals excel in aptitude assessments commonly used in academic and employment evaluations.

Company-wise guides offer detailed insights into specific companies, providing information on their culture, values, interview processes, and expectations. These guides assist individuals in preparing effectively for job applications and interviews tailored to the nuances of each company.

Mock interviews simulate real interview scenarios, helping individuals practice and refine their communication and problem-solving skills. These sessions provide constructive feedback, boosting confidence and readiness for actual job interviews in various fields.