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Data Scientist
Due to COVID 19 pandemic, there has been a spike in online classes. And there’s no better time than now for you to grab your online course on data science and get certified. This will help you start your job hunt once the situation becomes normal.
When you begin your online data science classes, you must check out the data science course syllabus. Crampete’s data science content offers you comprehensive modules that you need to learn to become job ready.
Crampete’s data science syllabus
Check out the details of the data science course offered by Crampete. We offer one of the best data science resources that you will ever find online. The course gives you an introduction to the basics of a data science career. Besides, it covers the six data science modules –
- Python for data analysis
- Intro to statistics
- Inferential statistics
- Regression and Anova
- Exploratory data analysis
- Supervised machine learning
This is a certified course and guarantees 100% job interview assistance. Learners including freshers, computer science or other engineering graduates, and working professionals from companies like TATA, HP, and Ford take up Crampete’s data science online course and learn advanced concepts designed in this comprehensive data science course syllabus.
This course is also open to beginners without any prior knowledge of data science or coding experience.
Crampete’s data science online lessons are a complete roadmap for you to prepare yourself for a great career.
About the course
The online course on data science offered by Crampete is a one-stop shop for all your data science learning needs. It offers you complete guidance and tutorial with downloadable resources and hands-on learning experience. The total duration of the course is 125 hours, and it can be attended from any location. There are over 200 videos and 100 quizzes to help you develop knowledge about the domain.
This course takes you through all the important modules that you need to know about, including machine learning and programming languages. It also teaches important concepts such as data acquisition, data mining, data processing, and data analysis.
This course is designed keeping in mind the current industry trends and skills required to become a successful data scientist. When you enroll for this course, you will receive one-to-one support from our instructors and practical-based learning experience. In this course, you will also receive complete support on how to go about job interviews and placements. Over 2000 students have benefited from our online data science course. Let’s see what some of our students have said about the course.
What is Data Science? An introduction
The term data science was coined at the beginning of the 21st century, and it is a fairly young field of science and technology. Over the past few decades, data science has increasingly become popular all over the world. It has become the part and parcel of every business model.
All forms of data run the world, be it personal data, website data, or behavioural data. With more and more Internet of Things being invented, we have shifted our focus to concepts like big data and data mining. Companies have started to capitalize on big data, which controls the way people think and act.
Data science has created a widespread impact across various sectors. In areas like healthcare, education, security, sports, energy, and science, the application of data science has created a lot of opportunities for innovation and improvement. Scientific studies rely on data sets to measure and analyze scientific goals, for example, healthcare recommendations, identifying and predicting diseases, personalized healthcare support via AI and machine learning,
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.
Introduction to the data science syllabus offered by Crampete
Crampete’s data science syllabus includes a comprehensive curriculum, which is designed on the basis of what most industries want from data science professionals. The data science syllabus is suitable for beginners, working professionals, or someone who wants to switch over to a career in data science.
There are six key modules [as mentioned earlier], which are further divided into nine lessons. These lessons cover a wide range of subjects – programming language, statistical tools, algorithms, and machine learning –
Module 1: Python
Python is the most important and necessary topic that every data scientist should have knowledge about. In this section, our instructors will take you through the basics of Python and areas where it can be used. You will learn how to use some of the current tools such as Numpy, Pandas, and Matplotlib. Therefore, module 1 includes –
- Environment set-up
- Jupyter overview
- Python Numpy
- Python Pandas
- Python Matplotlib
Module 2: R
Used for statistical and data analysis, R programming language is one of the advanced statistical languages used in data science. This module teaches you how to explore data sets using R. Here you will learn –
- An introduction to R
- Data structures in R
- Data visualization with R
- Data analysis with R
Module 3: Statistics
When working with data, the knowledge of statistics is necessary and an important skill set that you must have. In this module, you will learn –
- Important statistical concepts used in data science
- Difference between population and sample
- Types of variables
- Measures of central tendency
- Measures of variability
- Coefficient of variance
- Skewness and Kurtosis
Module 4: Inferential statistics
Inferential statistics is used to make generalizations of populations, from which samples are drawn. This is a new branch of statistics, which helps you learn to analyze representative samples of large data sets. In this module, you will learn –
- Normal distribution
- Test hypotheses
- Central limit theorem
- Confidence interval
- T-test
- Type I and II errors
- Student’s T distribution
Module 5: Regression and Anova
This lesson will help you understand how to establish a relationship between two or more objects. ANOVA or analysis of variance is used to analyze the differences among sample sets. Here you will learn –
- Regression
- ANOVA
- R square
- Correlation and causation
Module 6: Exploratory data analysis
In this lesson you will learn –
- Data visualization
- Missing value analysis
- The correction matrix
- Outlier detection analysis
Module 7: Supervised machine learning
This is a comprehensive module to help you understand how to make machines or computers interpret human language. You will learn –
- Python Scikit tool
- Neural networks
- Support vector machine
- Logistic and linear regression
- Decision tree classifier
Module 8: Tableau
Tableau is a sophisticated business intelligence tool used for data visualization. In this lesson, you will learn –
- Working with Tableau
- Deep diving with data and connection
- Creating charts
- Mapping data in Tableau
- Dashboards and stories
Module 9: Machine learning on cloud
In this lesson, you will learn –
- ML on cloud platform
- ML on AWS
- ML on Microsoft Azure
Each of these lessons are taught by instructors who have years of experience and knowledge of data science and analytics. We guarantee you one-to-one mentoring, and also support you with assessments and interviews towards the end of the session
Data science skills that you will master from this course
Besides data science skills, this course enables freshers and professionals to develop analytical and leadership skills. Additional skills that you will gain from this online course are –
- Learn new programming languages
- Learn to use frameworks based on tools like Hadoop and Apache Spark
- Learn about NLP and neural networks
- Gain hands-on experience on AI and machine learning tools
- Know all about python and various forms of tools used in programming languages, such as Python Numpy and Pandas.
- Learn how to use statistical models
- Know about exploratory data analysis and learn to measure and analyze data sets using visual patterns.
- Obtain one-to-one experience from instructors on supervised machine learning algorithms
- Develop leadership skills and understand how to make business decisions
- Understand data analytics and metrics important for any business
- Improve communication ability and become more confident
- Critical thinking and decision-making skills
Tools used to cover the topics in Crampete’s data science course
Data scientists require certain software tools for data operations. These tools are mostly statistical tools and programming languages used for data processing and analysis.
Some of the tools used in Crampete’s data science course are –
- Jupyter
- Python Pandas
- Python Numpy
- Python Matplotlib
- Statistical tools like T-test and ANOVA
- SAS
- Exploratory data analysis tools like Apache Spark, Tableau, etc.
- Excel
- Machine learning tools
- Python Ski-kit tool
- Software to learn about neural networks and fuzzy logic
- Natural Language Toolkit (NLTK), which are used by computers to interpret human languages.
Comparison of Crampete vs. other data science course syllabus
Crampete data science syllabus vs. IIT data science syllabus
If you want to study data science in India in any of the reputed institutions, then you can look for MS or M.Tech programmes offered by institutes such as IIT hyderabad, IIT Roorkee, and BITS Pilani.
Most of these institutes offer admission based on merit and your work experience. Also, these institutes offer a minimum of 1-year programme; therefore, the course fee increases with the duration of programme that you choose to study. In addition to all this, some of these institutes have certain eligibility criteria, which makes it difficult for professionals and students from other different backgrounds to take up data science courses with these institutes.
Also, the syllabus offered by these institutes is quite similar to what we offer on Crampete. Crampete focuses on essential topics like deep learning, Python, Apache Spark and other tools, and Big data. However, an IIT data science course might have additional topics because of the longer duration of the programme, such as data structures and algorithms, computer organization, operating systems, machine learning tools, and so on.
Crampete data science syllabus vs. MSc data science syllabus offered by various universities
If you wish to study masters in data science in any Indian universities and colleges, you will have to invest a minimum of 5 years. Other options open to you include diploma, integrated masters, bachelors, or certification courses in data science and analytics.
Most of the private and public universities and colleges offering data science and data analytics masters courses have comprehensive syllabi, which include the following:
- Knowledge of computer science and mathematics
- Modelling and abstract thinking
- Advanced theories
- Software tools
- Project management
- Communication skills
In addition, most of the courses have certain eligibility criteria for admissions, and the course fee ranges anywhere between INR 1.7 and 3.5 LPA.
[Did you know that institutes like Loyola College and Anna University in Chennai offer good quality courses in data science?]
Crampete data science syllabus Gds tech data science course syllabus
Gds tech ESMS offers several intensive data science courses, such as deep learning, python, statistics, Tableau, data analytics, etc. Each of these modules are further divided into different sections with assessments. This means you have to pay for each of the sections that you want to study online
What are the important areas in data science?
Data science is a vast field, and it offers a sea of opportunities for anyone who is interested to study. But, data science is not limited to the science of understanding the types of data available. There are several other components that you must understand if you want to be a data expert.
The other equally challenging and important modules in data science include –
- Data engineering
- Big data engineering
- Data analytics
- Database management
- Data mining
- Predictive analytics
- Machine learning or cognitive computing
- Data visualization, and so on.
The above-mentioned subjects should be part of every data science lesson that you are looking forward to going through.
Data science with python syllabus
Python is the most commonly used programming language in data science. Data professionals all over the world must understand the fundamentals of Python and Python libraries to learn advanced data science and data analysis techniques.
With Crampete, you can learn about Python fundamentals used in data science. Our course will help you jumpstart your career by providing you the required skill sets.
Our data science with Python syllabus comprises environmental set-up and the core software tools used such as Jupyter, Numpy, Pandas, and Matplotlib. Its all work front software of data science
Basic Requirements
In order to program with R and Python in the same Jupyter Notebook we need 3 basic things that will not be explained on this tutorial. These are:
- Have Python installed in version 3.5 or higher. If you don’t know which Python version you have, you can see how to check it here.
- Have R installed in version 3.2 or highger. You can see how to check your R version here.
- Having Jupyter installed.
If you don’t have Jupyter nor Python installed, I would recommed you to install them using Anaconda.
That was easy right? Now let’s dive into the interesting part;)
3. How to install R within Jupyter Notebook
Once you have Python, R and Jupter Notebook installed, you will have to install R in Jupyter Notebook. This will enable you to use R in Jupyter Notebooks. Apuesta Total
Yes, I know this is not much for R users as you could already write R Markdowns in RStudio which is somewhat similar to Jupyter… Unless you’re a Jupyter fan, of course.
In order to use R with Jupyter Notebooks you must install the packages within R essentials. To do this, you must run the following command line in Anaconda Prompt:
How to program with Python and R in the same Jupyter notebook
Python and R save their variables in different data types. So, if we want to use both languages together we will need a library that can “translate” Python lists to R vectors, for example.
To do so, we will use the rpy2 library. This library generates an interface in Python in which to work with R. Another option would be to use reticulate package that basically it does the opposite: it generates an interface in R in which to work with Python. If you are more an R programmer, you can find a tutorial on reticulate on this
Installing rpy2 library
First, we need to install the rpy2 library. To do so, we could install it directly from pip. However, sometimes it fails, so I wouldn’t recommend installing it this way. The way it has worked for me is:
1. Download the latest file (.whl) from here (Use Ctrl + F or Cmd + F to find it, since it is at the bottom of the page).
2. Install the file in Anaconda Prompt (or terminal for Mac users). To do this, go to the folder where it is downloaded with the code “cd path”. Once there, run “pip install [file name .whl]”. Example:
cd "C:\Users\Ander\Documents\Example"
pip install rpy2-2.9.5-cp37-cp37m-win_amd64
3. Add the path of the R bin folder to the PATH environment variable. To do this, the first thing you should do is find what is the path of the R bin folder. In my case, it is located at: “C: \ Program Files \ R \ R-3.6.1 \ bin”. Once you have it, you have to add it as a PATH environment variable. Here s how to do it, on both Windows and Mac.
Create the environment variables: R_HOME, R_USER and R_LIBS_USER. Each one will indicate where R is, who the user is and where the packages are, respectively. In each of them add the following:
- R_HOME: the root of R. In my case: C: \ Program Files \ R \ R-3.6.1 \
- R_USER: the users who will use R. If you want to use R beyond Jupyter, you must add two variables, the location of R Studio (C: \ Program Files \ RStudio \ bin) and the location of the rpy2 package (C: \ Users \ Ander \ Anaconda3 \ Lib \ site-packages \ rpy2)
- R_LIBS_USER: where the libraries are located. In my case: C: \ Users \ Ander \ Documents \ R \ win-library \ 3.6
After all, we simply launch a Jupyter Notebook in Python and load the library with the following command:
%load_ext rpy2.ipython
If everything is OK, congratulations! You can now use R within Python. How? It is super simple: every time you want to use a variable with R (for example, the dataframe df), you must “send” it to R using the following code:
%%R -i df
As a result, you can now operate with R inside Python. If you like this and don’t want to wait for more tutorials, here, you can dive into the rpy2 library. In any case, I leave you a notebook so you can see an example
Notebook Example on how to program with Python and R
%load_ext rpy2.ipython
import pandas as pd
import numpy as np
df = pd.DataFrame({
'cups_of_coffee': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
'productivity': [2, 5, 6, 8, 9, 8, 0, 1, 0, -1]
})
df.transpose()
What are the prerequisites for a data science course?
There is no predefined eligibility to be a data scientist. People from various disciplines can choose data science as a career option. But, if you have qualifications like an engineering degree in computer science, electronics, or IT, it will be easy for you to understand many concepts that are part of the course. Grade 12 students who also want to make a career in data science after finishing their studies can do so by applying for BS.c in data analytics or similar subjects.
Also, a certification or degree in data science, data analytics, or AI always gives an edge over others who do not have either of it. On the other hand, a data science certification can also open up opportunities for many beginners in this field. The average salary of a data scientist working in a big city like Mumbai can be around 10 Lakhs per year, which increases with experience in the field.
Duration and fee for a data science course
The duration of the course depends on your pace of learning. Normally, most online data science courses last for 20 weeks. On Crampete, the total duration of the data science course is 125 hours.
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