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Data Science using Python Training

data science using python
Python for Data Science Online Training is a most preferred course by programmers. Python is used in tech companies around the world, from startups to big corporate companies. Data scientists use python extensively for data analysis and insight generation. At the same time, many companies choose it for its ease of use, extensibility. It even includes readability, openness, and therefore the completeness of python’s standard library.

Python for Data Science Training Online

The Python for Data Science Certification Course from Coursack offers hands-on introduction to this programming language. That’s essential for the aspiring data scientists entering the field. Python programming skills are totally in a high demand. And learning it can be like open doors to the endless opportunities in the fields of data science, machine learning, web development and more. The Python for Data Science certification course teaches you to master the concepts of Python programming.

1 to 1 Instructor led Training

Group Classes

Corporate Training

Student Discount

  • 35+ Hours of Training.

  • Class for Individual Students

  • Labs and Assignments

  • Class Recording is provided

  • 35+ Hours of Training.

  • Training is provided to group of students

  • Labs and Assignments

  • Class Recording is provided

Looking for corporate training ? Contact us for group discount 

  • 35+ Hours of Training.

  • Training is provided to group of students

  • Labs and Assignments

  • Class Recording is provided

₹28,000

$475

₹22,500

$380

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Through our Python for Data Science Online training, you’ll gain knowledge in machine learning, data analysis, data visualization, web scraping, & NLP. Upon course completion, you’ll master the essential tools of knowledge Science with Python.

Who is the targeted audience?

This Python for Data Science course is useful for analytics professionals willing to figure with Python, Software, and IT professionals curious about the field of analytics. Anyone with a genuine interest in Data Science can learn this training. The demand of professionals in Python for Data Science have surged and made this course well suited for the participants at all levels of experience.

1. Python Essentials

1
– Overview of Python- Starting with Python
2
– Introduction to installation of Python
3
– Introduction to Python Editors & IDE’s (Canopy, PyCharm, Jupyter, Rodeo, Ipython etc…)
4
– Understand Jupyter notebook & Customize Settings
5
– Concept of Packages/Libraries – Important packages (NumPy, SciPy, scikit-learn, Pandas, Matplotlib)
6
– Installing & loading Packages & Name Spaces
7
– Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
8
– List and Dictionary Comprehensions
9
– Variable & Value Labels – Date & Time Values
10
– Basic Operations – Mathematical – string – date
11
– Reading and writing data
12
– Simple plotting
13
– Control flow & conditional statements
14
– Debugging & Code profiling
15
– How to create class and modules and how to call them?

2. Scientific Distribution

1
– Numpy, scify, pandas, scikitlearn, statmodels, nltk

3. Accessing / Importing and Exporting Data using Python modules

1
– Importing Data from various sources (Csv, txt, excel, access etc.)
2
– Database Input (Connecting to database)
3
– Viewing Data objects – subsetting, methods
4
– Exporting Data to various formats
5
– Important python modules: Pandas, beautifulsoup

4. Data Manipulation

1
– Cleansing Data with Python
2
– Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting etc.)
3
– Data manipulation tools (Operators, Functions, Packages, control structures, Loops, arrays etc.)
4
– Python Built-in Functions (Text, numeric, date, utility functions)
5
– Python User Defined Functions
6
– Stripping out extraneous information
7
– Normalizing data
8
– Formatting data
9
– Important Python modules for data manipulation (Pandas, NumPy, re, math, string, datetime)

5. Visualization using Python

1
– Introduction exploratory data analysis
2
– Descriptive statistics, Frequency Tables and summarization
3
– Univariate Analysis (Distribution of data & Graphical Analysis)
4
– Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
5
– Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density)
6
– Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats)

6. Introduction to Predictive Modeling

1
– Concept of model in analytics and how it is used?
2
– Common terminology used in analytics & modeling process
3
– Popular modeling algorithms
4
– Types of Business problems – Mapping of Techniques
5
– Different Phases of Predictive Modeling

7. Modeling on Linear Regression

1
– Introduction – Applications
2
– Assumptions of Linear Regression
3
– Building Linear Regression Model
4
– Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis)
5
– Assess the overall effectiveness of the model
6
– Validation of Models (Re running Vs. Scoring)
7
– Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers)
8
– Interpretation of Results – Business Validation – Implementation on new data

8. Modeling on Logistic Regression

1
– Introduction – Applications
2
– Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
3
– Building Logistic Regression Model (Binary Logistic Model)
4
– Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve)
5
– Validation of Logistic Regression Models (Re running Vs. Scoring)
6
– Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance)
7
– Interpretation of Results – Business Validation – Implementation on new data

9. Time Series Forecasting

1
– Introduction – Applications
2
– Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition
3
– Classification of Techniques (Pattern based – Pattern less)
4
– Basic Techniques – Averages, Smoothening
5
– Advanced Techniques – AR Models, ARIMA
6
– Understanding Forecasting Accuracy – MAPE, MAD, MSE
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Enrolled: 225 students
Duration: 40 Hours
Lectures: 62
Level: Beginner

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