Data Science with Python Online Training in India
Data Science with Python Online Training
Aarushit provides comprehensive Data Science with Python online training designed to equip students and professionals with in-demand analytical and technical skills. The program covers essential topics such as Python fundamentals, data manipulation with Pandas and NumPy, data visualization, statistical analysis, and machine learning using industry-relevant tools and real-time datasets. With expert trainers, hands-on projects, and practical case studies, Aarushit ensures learners gain both theoretical knowledge and practical exposure. The flexible online format allows participants to learn at their convenience while receiving personalized guidance and career support to help them transition into data science roles confidently.
What is Data Science ?
Data Science with Python is a widely adopted approach for analyzing data, building predictive models, and extracting meaningful insights from structured and unstructured datasets. Python’s simplicity and rich ecosystem of libraries make it especially suitable for data analysis, visualization, and machine learning tasks. Tools such as Pandas and NumPy are used for data manipulation, while Matplotlib and Seaborn help create visual representations of data. For machine learning and statistical modeling, libraries like Scikit-learn and TensorFlow provide powerful capabilities. With its strong community support and versatility, Python has become one of the most preferred programming languages for data scientists working in fields such as finance, healthcare, marketing, and artificial intelligence.
Data Science with Python Online Training Course Content
MODULE 1: Introduction To Python – Data Science
Installation of Anaconda setup (Data Science Development Environment)
Installation of Pycharm
Working with Python List , List operation , Functions
Python Tuple , working and functions
Sets and Dictionary -operations and Working with them
Python More on Strings
Python Dates and Times
More on functions
Advanced Python Lambda
List Comprehensions
MODULE 2: Data Analysis
- Data Wandering
All about files Files
importing and exporting data with CSV files
XLRD module – working with xls .xlsx formats
Json data
XML data
Relational data Bases
Sql in python
Data quality Analysis - DATA MANIPULATION – Cleaning – Munging – Cleansing Data with Python
strong>Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
Python Built-in Functions (Text, numeric, date, utility functions)
Python User Defined Functions
Stripping out extraneous information
Normalising data
Formatting data
Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc) - DATA VISUALIZATION
Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc) - DATA ANALYSIS WITH PANDAS
The Series Data Structure
Querying a Series
The Data-Frame Data Structure
Data-Frame Indexing and Loading
Querying a Data-Frame
Indexing Data-frame
Understanding business problem
Selecting columns from Pandas Data Structures
Treating with missing values, outliers, NaN values
Creating new columns
Aggregate data ( use: groupby, merge, pivot, lambda)
Identifying unique values in data
Filter Data
Using basic functionality of Pandas API
MODULE 3: Mathamatics
- STASTISTICS
Basic Statistics – Measures of Central Tendencies and Variance
Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
Inferential Statistics -Sampling – Concept of Hypothesis Testing
Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
Important modules for statistical methods: Numpy, Scipy, Pandas - PROBABILITY
Probability , Conditional Probability
Basic of Probability, Independent and Dependant events
Conditional Probability and Bayes Theorem
Continuous Probability Distributions
Mean, Median, Mode, Range
Determination of statistical techniques
Standard Deviation, Variance, Covariance, Correlation
outliners
Distribution of Data – Normal, Binomial, Gaussian
Different types of Data
Continuous , Categorical, Range
Testing of Hypothesis – which covers
Level of Significance (LOS), Level of Confidence, P-Value, T test, Z-test, ANOVA Test, CHI -Square Test
MODULE 4: Machine Learning
- SUPERVISED LEARNING AND MODEL BUILDING
Process of Machine Learning
Model Building based on Data sets
Splitting Data: Training and Test sets
Regression Analysis (Linear, Multiple, Logistics Regression)
Classification concepts and Distance Functions
K-nn Algorithm concept and demonstration with data sets
Bayes Classification concept and demonstration with data sets
Decision Tree Algorithm concept and demonstration with data sets
Random Forests – Ensembling Techniques and Algorithms - UNSUPERVISED LEARNING AND MODEL BUILDING
Unsupervised Learning and Clustering Techniques
Centroid-based Clustering: K- Mean Algorithm concept and demonstration
Hierarchical Clustering concepts and Applications
Density-based Clustering: DBSCAN Algorithm concept and demonstration - DIMENSION REDUCTION TECHNIQUES
Dimension Reduction Introduction
Why Dimension Reduction Required
LDA (Linear Discriminant Analysis) concept and applications
PCA (Principle Component Analysis) concept and applications - TIME SERIES FORECASTING: SOLVING FORECASTING PROBLEMS
Introduction – Applications
Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
Classification of Techniques(Pattern based – Pattern less)
vBasic Techniques – Averages, Smoothening
Advanced Techniques – AR Models, ARIMA - DATA SCIENCE PROJECTS WITH DATA SETS
Applying different algorithms to solve the business problems and bench mark the results
Data Science with Python Course Information
Training Level : Advanced
Online Training Duration : 60 Hours
Class Size : Limited
Email ID : revanthonlinetraining@gmail.com
Contact No : +91 9885596246 // 7893762206
WhatsApp No : +91 7893762206
