Course Overview

Course Duration : 6 Months

Data scientist is one of the top, in-demand professions not just in India, but all over the world. One of the best ways you can pursue this career is through DigiAcharya’s
Data Science with Python course. Python is one of the best high-level open-source languages for object-oriented programming. Another advantage of Python is that you do not require engineering or coding experience. Therefore, data science with python is one of the best ways to become a data scientist.
DigiAcharya’s detailed and comprehensive data science with Python course helps you become an expert with data gathering, cleaning, and manipulation. You will also get to work with Python libraries like Matplotlib and NumPy. This course is one of the best ways to get started in the data science field and become an expert and in-demand data scientist. Our syllabus has been designed by the finest professionals who are in tune with data science and other industry needs.

Our program is suitable for

If you are interested in pursuing DigiAcharya’s data science with Python, you need to have cleared your higher secondary (10+2) with 50% or more. Working professionals can also be eligible to pursue this course. Though not compulsory, if you are good with mathematics and physics, you will be able to pick up data science a little quicker. However, DigiAcharya’s data science with Python is crafted in a way that you can easily understand and excel in the subject.

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Course Module

Foundation for Data Science

The internet is a crowd of structured and unstructured data. Data science is a subject that deals with this scattered data through scientific methods, processes and algorithms. In module 1 of the Data Science with Python course, students shall be introduced to the fundamentals of data science and how it is a prominent area of study in the digital world. They also learn of the varied data science sectors alongside the analytics landscape. One can also imbibe the data science tools and technologies and learn about the life cycle of a Data Science project.

Data Analytics Overview

Data Analytics is about analysing and processing raw data to retrieve a meaningful conclusion out of it. Module 2 of Data Science with Python introduces students to the process of data analytics. There is also a section dealing with knowledge checks. The syllabus also provides explanatory data analysis (EDA) with quantitative and graphical techniques. Towards the end, students grasp the process of deriving conclusions and predictions through data analytics. They also learn about data types for plotting.

Statistical Analysis and Business Applications

Statistical analysis is the method of analysing the data through statistics to derive the actual meaning that it attempts to convey. Module 3 of Data Science with Python introduces the students to the basics of statistics and teaches statistical and non-statistical analysis. The module also takes you through significant categories of statistics. Population, sampling and other statistical analysis processes are also included in the course. Towards the end of this section, students learn about data distribution, dispersion, Histogram, testing and more.

Probability & Statistics

Probability is sometimes referred to as the backbone of the data science course. It is because there are multiple elements in the course that require a basic knowledge of probability. Module 4 of the Data Science with Python course deals with probability and statistics. To begin with, students learn the measures of central tendency and dispersion. They also study descriptive statistics. The basics of probability follow right after. Later students know about marginal probability, Bayes Theorem, probability distributions and hypothesis testing.

Basics of Python

Python, the high-level programming language, is high in demand due to its simplicity and readability. It can fetch job offers like no other course. Students from Digi Acharya’s Data Science with Python course begin to learn the language in module 5. It starts with the installation of Anaconda that helps in learning the language. The course proceeds with the purpose and components of Python. Students also learn the data types and variables. String & regular expressions are also in this section.

Python Built-in Data Structures

A few basic built-in data structures in Python enable one to store and access data efficiently. With module 6 of the Data Science with Python course at Digi Acharya, students learn a great deal about these built-in structures, their types, functioning and more. The python list, directories, set and tuple, are all part of this curriculum. Module 6 ends a detailed rendition of comprehensions to the students.

Control & Loop Statements in Python

Control and loop statements in Python help to alter the flow of execution. Learning these in module 7 of Data Science with Python helps students learn how to alter, skip and break the iteration. It becomes easier to break statements and repeat them if necessary. Students also learn to switch statements and the detailing of if-else statements.

Functions & Classes in Python

When one begins to code, if you wish to manipulate or change something, it becomes an object. The classes in Python is the outline for building a new object. In module 8 of Data Science with Python, students learn about the classes and their varied functions. Beginning with writing and calling your own python functions, students learn Python’s varied functions and arguments. Calling Python Functions by passing Arguments are towards the end. Lambda Functions and classes, and objects are also in the course.

Mathematical Computing with Python (NumPy) ∙

NumPy helps solve a wide variety of arithmetic problems in an array. Module 9 of the Data Science with Python course allows students to learn the basics of NumPy. There is also an extravagant knowledge check in this section. Students also know about the class and attributes of NumPy ndarray. Basic operations of NumPy, copy, and views are also in the course. The module ends with the mathematical Functions of Numpy.

Scientific computing with Python (Scipy)

As the python programming language proceeds to model and solve scientific problems, it requires a library of numerical routines, i.e., SciPy, to perform the task. In module 10 of Data Science with Python, we learn about SciPy and its properties. The integration and optimization in the SciPy Sub package are also in the section. Later, students know more about the subpackage, including statistics, weave, and IO.

Working with Data & Data Manipulation with Pandas

Pandas are one of the prominent libraries in the data science workflow. In the data manipulation genre, they are of great importance. Module 11 of Data Science with Python course revolves around these aspects. Students begin this section with reading and writing files with Python. There is an elaborate introduction to the vast Pandas library. Understanding data frame and operations are also in the curriculum. One also learns to sequence the files alongside the Pandas SQL Operation.

Analyze Data using Pandas

As the students become thorough with a few basics of Pandas, the next modules reveal a deeper insight into the subject. Beginning with learning to clean & prepare datasets, students proceed to manipulate DataFrame. They also learn how to summarize data and extract insights out of it. 

Visualize Data

Module 13 of the Data Science with Python course at the Digi Archarya is about data visualisation. It commences with the introduction to Data Visualization and line properties. They learn about plots, sub-plots and their various types. Towards the end of this module, students define charts using Matplotlib, Seaborn, and ggplot.

Advanced Statistics & Predictive Modeling

Predictive modelling is often used to predict future behaviour. In module 14 of Data Science with Python, students grab an idea about  Analysis of Variance or ANOVA. They also know linear regression (OLS) accompanied with a case study for better understanding. The principal component analysis, factor analysis, and a detailed case study come next in this context. The section has a detailed study about logistic regression (MLE) and its case study. The course moves to the K-Nearest neighbour algorithm, Decision Tree and their respective case studies.

Time Series Forecasting

Time series forecasting allows programmers to predict future data with the help of current and past information. Module 15 of the Data Science with Python course helps understand the time series data. Visualizing time series components is a subsection of this module, allowing to split and disseminate efficiently. Exponential Smoothing, Holt’s Model, Holt-Winter’s Model and ARIMA are towards the finish of this module.

Introduction to Machine Learning & Machine Learning with Scikit–Lear

Machine learning is a vital skill gained after the Data Science with Python course. Module 16 begins with an introduction to machine learning and understanding its approach. This is followed by understanding data sets and extracting its features alongside identifying problem types and learning models. Supervised and unsupervised learning is another important aspect of machine learning. Students also know how to train, test and optimize the model. Supervised learning model considerations and the usage of Scikit-learn are other important sections of this course. The section has a lot more to offer than what is explained here.

Natural Language Processing with Scikit Lear

Natural Language Processing or NLP is a system to act according to written or spoken language.  This branch of data science is an exciting sub-field that allows building high performing day to day apps.  Module 17 of the Data Science with Python course renders an overview regarding NLP and its applications.  The NLP Libraries-Scikit is also in the curriculum.  The module ends with extraction considerations and Scikit Learn-Model Training and Grid Search.

Web Scraping with BeautifulSoup

Web scraping is the art of extracting raw information from data utilizing it for organization, and attaining meaningful information. In this module 19 of Data Science with Python, students learn the fundamentals of web scraping and parsing. They understand the need for searching the tree to navigate raw data. Modification of the tree is also taught towards the end of this module, along with parsing and printing the document.

Python integration with Hadoop MapReduce and Spark

The last module of the Data Science with Python course is about Python integration with Hadoop MapReduce and Spark. In this section, students learn the importance of Big Data for PYthon programming. They proceed to know the Hadoop core components and the integration of Python with Spark using the PySpark.

Data Science with Python Platforms & Tools

In the field of data science, almost 46% of the jobs are for professionals with proficiency in Python. It has become a necessary skill to obtain. The demand has increased with students learning the craft and working for renowned platforms.

NumPy

SciPy

Matplotlib

Scikit Learn

TensorFlow

Pandas

What skills you will gain

Statistics

Statistics is a skill that trains a person’s mind to analyse problems as a hypothesis and utilise the course study to solve them. It is the scientist way of thinking. With the Data Science with Python course, students cultivate the statistics skill that helps them generate a solution for every problem.

Python

Python, a high-level programming language, is the key element of this course. It is easily readable and extremely user-friendly. Hence, there is a lot of demand for the same. With the Data Science with Python course, students verse themselves in this skill. With Python as a skill-in-hand, one can easily ace machine & deep learning.

Data Wrangling

The data wrangling skills obtained from the Data Science with Python course enable the student to quickly and efficiently clean and unify the complex data sets. Students learn to map data from one form to another for better organization skills.

Web scraping

Another vital skill obtained from Data Science with Python course is web scraping. Students learn to extract content and data from websites with the help of bots. Students can utilize HTML codes available in the database with web scraping skills. Through this web, cloning is possible to be used elsewhere.

Machine Learning

Machine and deep learning is another crucial skill in the Data Science with Python course. Here they imbibe the ability to use the machine and deep learning in their job frequently. With completing this course, students cover all the machine learning approaches and techniques.

Statistics

Statistics

Statistics is a skill that trains a person’s mind to analyse problems as a hypothesis and utilise the course study to solve them. It is the scientist way of thinking. With the Data Science with Python course, students cultivate the statistics skill that helps them generate a solution for every problem.

Python

Python

Python, a high-level programming language, is the key element of this course. It is easily readable and extremely user-friendly. Hence, there is a lot of demand for the same. With the Data Science with Python course, students verse themselves in this skill. With Python as a skill-in-hand, one can easily ace machine & deep learning.

Data Wrangling

Data Wrangling

The data wrangling skills obtained from the Data Science with Python course enable the student to quickly and efficiently clean and unify the complex data sets. Students learn to map data from one form to another for better organization skills.

Web scraping

Web scraping

Another vital skill obtained from Data Science with Python course is web scraping. Students learn to extract content and data from websites with the help of bots. Students can utilize HTML codes available in the database with web scraping skills. Through this web, cloning is possible to be used elsewhere.

Machine Learning

Machine Learning

Machine and deep learning is another crucial skill in the Data Science with Python course. Here they imbibe the ability to use the machine and deep learning in their job frequently. With completing this course, students cover all the machine learning approaches and techniques.

Career Roles

The Data Science with Python is an advanced course that defines your career with job-ready skills. Some of these opportunities are described here.

Be a Data Scientist at leading platforms

Invest your top analytical skills with the best companies

Venture the government sector as an IT professional

Serve the globe by your high-quality data science skills.

Want to have an ever-blooming career with desirable paychecks?

Want to have an ever-blooming career with desirable paychecks?

FAQ's

Data Science with Python is a comprehensive curriculum enabling students to master Python programming and data analysis, manipulation and visualization.

If you know coding, it is easier to learn Python. However, it is not compulsory as we begin our courses from the fundamental level.

Python is not a programming language that requires the foundation of some other language. Additional knowledge of these languages helps you learn better. However, they are not compulsory.

Absolutely not. The Digi Acharya’s curriculum for Data Science with Python is built to render both beginners and professionals. It is easily understandable provided you are zealous to learn.

Yes, we offer certifications with global credibility.