Data Science & Analytics with AI Course

Data Science & Analytics with AI Course is a dynamic and rapidly evolving field that combines statistical analysis, machine learning, and programming to extract valuable insights and knowledge from large and complex datasets. Python, a versatile and user-friendly programming language, serves as a powerful tool for data scientists, providing a wide range of libraries and frameworks specifically designed for data manipulation, visualization, and modeling.

In this field, practitioners use Python to collect, clean, analyze, and visualize data, uncover patterns, build predictive models, and make informed decisions. With its extensive ecosystem, Python empowers data scientists to tackle real-world problems across various industries, from finance and healthcare to marketing and social sciences. By leveraging Python's flexibility and the abundance of open-source tools, data scientists can unleash the potential of data and drive innovation in today's data-driven world.

Benefits Of Data Science

Rich Ecosystem of Libraries: Python provides a vast collection of libraries and frameworks specifically designed for data science, such as NumPy, pandas, Matplotlib, and scikit-learn.
Ease of Use and Readability: Python has a clean and readable syntax, making it beginner-friendly and easy to learn.
Broad Adoption in the Data Science Community: It is widely used by researchers, analysts, and data scientists across various industries. Its popularity ensures a strong support network and a vast array of online resources, tutorials, and libraries created by the community.
Powerful Data Manipulation and Analysis: Python's libraries, such as NumPy and pandas, provide efficient and flexible data structures and functions for data manipulation and analysis.
Integration with Machine Learning Libraries: Python serves as a bridge to popular machine learning libraries like scikit-learn, TensorFlow, and PyTorch.

Course Content

Advanced Data Wrangling with Pandas and Numpy

Pandas Data Frames and Series
Merging, Joining, and Concatenating Data Frames
Reshaping and Pivoting Data Frame
Handling Missing Data and Data Imputation
Hierarchical Indexing and Multi-level Indexing
Time Series Analysis with Pandas
NumPy arrays: Creation, indexing, and slicing
Basic operations with NumPy arrays: Arithmetic, broadcasting, and aggregations
Universal functions (ufuncs) in NumPy
Working with multi-dimensional arrays

Advanced Data Visualization with Plotly and Bokeh

Introduction to Plotly and Bokeh
Plotting with Plotly
Interactive Visualizations with Plotly
Creating Dashboards with Plotly
Creating Interactive Web Applications with Bokeh
Installing Matplotlib and setting up the environment
Anatomy of a Matplotlib figure: Figure, axes, and subplots
Basic plotting: Line plots, scatter plots, and bar plots
Customizing plot appearance: Colors, markers, line styles, labels, and titles

Probability Distributions
Bayesian Statistics
Markov Chain Monte Carlo (MCMC) Simulation
Hypothesis Testing with Python
ANOVA and MANOVA
Non-parametric Methods

Introduction to Time Series Analysis
Handling Time Series Data with Pandas
Time Series Visualization
Time Series Decomposition and Trend Analysis
Autoregressive Integrated Moving Average (ARIMA) models
Prophet

Introduction to Natural Language Processing
Text Processing with Python
Regular Expressions and Text Normalization
Sentiment Analysis and Text Classification
Topic Modeling with Latent Dirichlet Allocation (LDA)
Word Embeddings with Word2Vec

Advanced Regression Techniques
Regularization Techniques: Ridge, Lasso, Elastic Net
Gradient Boosting Machines (GBMs)
Support Vector Machines (SVMs)
Clustering Techniques: K-means, Hierarchical Clustering
Dimensionality Reduction Techniques: Principal Component Analysis (PCA), t-SNE

Introduction to Deep Learning
Neural Networks
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Autoencoders
Generative Adversarial Networks (GANs)

Introduction to Big Data and PySpark
RDDs, DataFrames, and Datasets
PySpark SQL and Spark MLlib
Distributed Computing with PySpark
Working with Spark on Cloud Platforms

Introduction to Power BI
Transforming Data with Power BI Desktop
Data Modeling with Power BI
DAX
Visualising data with reports
Introduction to the Power BI Service
Sharing & Collaboration Tools

Head Office Address

201, Anant Laxmi Chambers,
Opp Waman Hari Pethe Jwellers,
Dada Patil Wadi
RoadThane (W),
Maharashtra 400602

BRANCHES

THANE : +91 8422800381
enquiry@quastech.in
BORIVALI : +91 8422800384
enquiry.borivali@quastech.in
VASHI : +91 8422800383
enquiry.vashi@quastech.in
MOHALI : +91 7208008461
enquiry.mohali@quastech.in
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Data Science & Analytics with AI Course

Benefits Of Data Science

Course Content

Head Office Address

BRANCHES

COURSES OFFERED