1 to 2 Months
Online + Offline
15 Students Only Batch Size
Anyone
Beginner to Advance Training
Data science focused on extracting insights from both structured and unstructured data. It integrates statistics, mathematics, computer science, and domain expertise to solve complex problems and inform decision-making. Key Concepts, Definition: Data science involves extracting insights from data. Purpose and Scope: Understanding the broad applications of data science. Multidisciplinary Nature: Recognizing the diverse skill set required. Role in Decision-Making: Emphasizing its contribution to informed decision-making. Problem-Solving: Addressing real-world challenges through data analysis.
Probability Basics: Definition: Likelihood of an event occurring. Laws: Principles governing probability calculations. Random Variables: Understanding variables and their distributions. Statistical Concepts: Central Limit Theorem: Distribution of sample means tends to be normal. Hypothesis Testing: Evaluating assumptions about a population parameter. Bayesian Inference: Updating probabilities based on new evidence. Markov Chains: Stochastic processes with the Markov property. Monte Carlo Simulations: Using random sampling for numerical results. Relevance in Machine Learning: Descriptive Statistics: Summarizing main features of a dataset. Probability Distributions: Models for random phenomena. Inferential Statistics: Making predictions about a population. Regression and Correlation: Analyzing relationships between variables. Experimental Design and A/B Testing: Conducting controlled experiments. Time Series Analysis: Analyzing data collected over time.
Python Essentials: Setting Up Python: Configuring Python in different Integrated Development Environments (IDLE, VSCode, PyCharm). Basics of Python Programming: Covering fundamental concepts like numbers, arithmetic, printing, operators, strings, variables, input handling, conditionals, and loops. Advanced Python Concepts: Advanced Operations: Covering split/join/slice operations, working with files, directories, paths, and CSV data manipulation with Pandas. Comprehensions: Efficient ways to create lists, dictionaries, and other iterable structures. Functions and Lambdas: Understanding first-class functions and using lambda expressions. Imports and Organizing Code: Proper use of imports, mapping, filtering, and organizing code with iterators and generators.
Environment Setup: Install Anaconda Python: Includes Python, Jupyter, and data science libraries. Configure Jupyter: Setup for seamless data science environment. NumPy Fundamentals: Array Operations: Create arrays, index, slice, and perform essential functions. Reshaping Arrays: Techniques for reshaping arrays for different analyses. Pandas Introduction: Data Structures: Overview of Pandas structures, with a focus on DataFrames. Operations on DataFrames: Load/save data and perform essential manipulations. Matplotlib and Seaborn Basics: Data Visualization: Introduction to basic concepts. Create Plots and Visualizations: Use Matplotlib and Seaborn for various plot types.
Practical Applications: Conducting Analysis: Apply concepts to analyze real datasets. Forming Hypotheses: Develop hypotheses based on initial data exploration. Data Preprocessing and Wrangling: Data Collection Techniques: Strategies for collecting relevant data. Handling Missing Values and Outliers: Techniques for addressing anomalies. Data Cleaning: Approaches to clean and prepare data for analysis. Feature Engineering: Data Transformation: Techniques for converting raw data into a usable format. Scaling and Integration: Rescale features and integrate data from various sources. Reduction and Aggregation: Reduce dimensionality and aggregate data for analysis. Comprehensive Skill Set: Hands-On Skill Development: Practical exercises for a comprehensive skill set. Application of Techniques: Integrate learned techniques into real-world data science scenarios.
Once you have completed the course you will be able to generate the certificate
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