2 to 3 Months
Online + Offline
15 Students Only Batch Size
Anyone
Beginner to Advance Training
Data science is a field that involves extracting insights from structured and unstructured data. It incorporates various disciplines, including statistics, mathematics, computer science, and domain expertise, to solve complex problems and make informed decisions. Key Concepts Definition of Data Science,Understanding the purpose and scope of data science. Multidisciplinary Nature: Recognizing the diverse skill set required in data science. Role in Decision-Making: Emphasizing the role of data science in aiding decision-making processes. Problem-Solving: Addressing real-world challenges through data analysis and interpretation.
Probability Basics: Definition of Probability: The likelihood of an event occurring. Laws of Probability: Principles governing probability calculations. Random Variables and Distributions: Understanding variables and their probability distributions. Statistical Concepts: Central Limit Theorem: The 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 to obtain numerical results. Relevance in Machine Learning: Descriptive Statistics: Summarizing and describing main features of a dataset. Probability Distributions: Models for random phenomena. Inferential Statistics: Making predictions or inferences 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: Installing Anaconda Python: A distribution that includes Python, Jupyter, and essential data science libraries. Configuring Jupyter: Setting up Jupyter notebooks for a seamless data science environment. NumPy Fundamentals: Array Operations: Creating arrays, indexing, slicing, and essential functions. Reshaping Arrays: Understanding techniques to reshape arrays for different analyses. Pandas Introduction: Data Structures: Overview of Pandas data structures, with a focus on DataFrames. Operations on DataFrames: Loading and saving data, as well as essential manipulations. Matplotlib and Seaborn Basics: Data Visualization: Introduction to basic data visualization concepts. Creating Plots and Visualizations: Using Matplotlib and Seaborn for creating various types of plots and visualizations.
ractical Applications: Conducting Analysis: Applying learned concepts to analyze a real dataset. Forming Hypotheses: Developing 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 missing or unusual data points. Data Cleaning: Approaches to clean and prepare data for analysis. Feature Engineering: Data Transformation: Techniques for transforming raw data into a usable format. Scaling and Integration: Rescaling features and integrating data from various sources. Reduction and Aggregation: Reducing dimensionality and aggregating data for analysis. Advanced Techniques: Binning and Encoding Categorical Data: Handling categorical variables. Text Data Preprocessing: Managing and analyzing text data. Date, Time, and Geospatial Data: Techniques for handling and analyzing temporal and spatial data. Comprehensive Skill Set: Hands-On Skill Development: Practical exercises to ensure a comprehensive skill set. Application of Techniques: Integrating learned techniques into real-world data science scenarios.
Once you have completed the course, assignments, exercise and submit the projects you will be able to generate the certificate and be eligible for placements
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