Skip to main content

Complete 3 MONTHS Free Courses for Data Science Beginners Part 1

Using massive datasets to guide decision is becoming more and more important for modern businesses. with over six times faster growth than other industries, data analytics and data science has become the fastest growing field today. if you are looking for a rewarding career, data science can be a great field. There has been a huge demand for data scientist across the US, EUROPIAN UNION and other countries including India, JAPAN, CHINA  and so on.
But most difficult part, if you are beginner, is that from where you can start, starting few days is crucial for anything so starting with data science, following for a life long you need to have proper set of knowledge from where to begin , so here I am listing few open source data sciences course, a 12 week complete study plan which can really help you to take entry in the fields like data science.

WEEK 1:

For starting with data science you should have good command on python libraries, which are extremely important for data science. python libraries like Numpy, matplotlib and Pandas are most important. So to begin with PYTHON for data science there are many courses available but there is one complete package for data science python. there is a course with the name "Introduction to Python for Data Science"  by Microsoft on edx  platform which is exceptionally great for beginners in data science, do complete practice of this course for first week and get your hands sharp on these python libraries , do practice as much as you can so that you get clarity on basics  programming for data science. here we are also providing a link to that course which you can use to directly go to that course.

WEEK 2:

There are different things that you need to learn while going with this course along with good python programming knowledge you should also have concept clarity about the topics in maths like Statics and Probability. While analysing data in real-world, you need to apply these concepts to filter the data
so along with python you to learn these concepts as well and one best platform to learn these mathematical concepts is Khan Academy. you can learn statics and probability from khan academy.
there are so many concepts on khan academy try to learn basics first so that you can understand the things better while working on data science problems.
my suggestion while doing this part is, focus on the concepts, it might be possible you get bored and distracted while doing this course but believe me if want to learn data science you need to focus on these things. Link to this course is given below :


WEEK 3:

Now after doing these 2-week courses you will get to know what data science is all about and at this point, you need to get your hands on some practical problems and some advancement of data science skills and in this complete week you have to deal with two courses, again first course is by Georgia tech on edx with the name "Introduction to computing for data analysis". and second is you need to practice basic problems on KAGGLE platform, its platform where a data science professional works. Link to this course is given below:
                        https://www.edx.org/course/computing-in-python-i-fundamentals-and-procedural-programming-0

HERE WE ARE PROVIDING COMPLETE VIDEO FROM WHERE YOU CAN GET MORE INFORMATION ABOUT 3 MONTHS DATA SCIENCE COURSE:
We Will telling you more about it in other parts of this Article, till then stay tuned and if you like our work please do follow.



Comments

Post a Comment

Leave a Message !

Popular posts from this blog

Complete Tutorial on Business Analytics

CRISP-DM (Cross Industry Standard Process for Data Mining)                                    The framework is made up of 6 steps: Business Issue Understanding Data Understanding Data Preparation Analysis/Modeling Validation Presentation/Visualization The map outlines two main scenarios for a business problem: Data analysis Predictive analysis Data analysis refers to the more standard approaches of blending together data and reporting on trends and statistics and helps answer business questions that involve understanding more about the dataset such as "On average, how many people order coffee and a donut per transaction in my store in any given week?" Predictive analysis will help businesses predict future behavior based on existing data such as "Given the average coffee order, how much coffee can I expect to sell next week if I were to add a new brand of coffee?" Business Issue Understanding "This initial phase focuses on understanding the project objectives a
Post a Comment
Read more

What is a Type II Error?

  A Type II error is a false negative in a test outcome, where something is falsely inferred to not exist. This usually means incorrectly accepting the null hypothesis (H0), which is the testing statement that whatever is being studied has no statistically significant effect on the problem. An example would be a drug trial that incorrectly concludes the prescribed medication had no effect on the patient’s ailment, when in fact the disease was cured, but subsequent exams caused a false positive showing the patient was still sick. Null Hypothesis and Statistical Significance In practice, the difference between a false positive and a false negative is usually not so clear-cut. Since the tests are most often quantitively rather than qualitatively based, the results tend to be expressed in a confidence interval value less than 100%, rather than a simple Yes/No decision. This question of how likely the results are to be found if the null hypothesis is true is called statistical significance.
Post a Comment
Read more

The Art of Winning Kaggle Competitions

 What is Kaggle? Learning data science can be overwhelming. Finding a community to share code, data, and ideas can se e m also seem like an overwhelming as well as farfetched task. But, there is one spot where all of these characteristics come together. That place is called Kaggle. Looked at more comprehensively, Kaggle is an online community for data scientists that offers machine learning competitions, datasets, notebooks, access to training accelerators, and education. Anthony Goldbloom (CEO) and Ben Hamner (CTO) founded Kaggle in 2010, and Google acquired the company in 2017. Kaggle competitions have improved the state of the machine learning art in several areas. One is mapping dark matter; another is HIV/AIDS research. Looking at the winners of Kaggle competitions, you’ll see lots of XGBoost models, some Random Forest models, and a few deep neural networks. The Winning Recipe of Kaggle Competitions involves the following steps: Step one   is to start by reading the competition gu
Post a Comment
Read more