This can be a super guide for you to start and excel in your data science career.
Break the ice
This can be a super guide for you to start and excel in your data science career. A lot of times we are either bombarded with inadequate information/ content which is partially industry oriented. In most cases, the effectiveness of a professional is determined on the basis of his/her practical experience. That is why one’s skill development process should involve hands-on experience of tools and languages, knowledge of enough use cases, thirst of implementing your knowledge and experience for getting a job profile in Data Science.
The pillars of data science professionals are
- Reporting tools like Excel, Google sheets
- Business/ Domain Knowledge
- SQL skills
- BI tools skills
- Python/ R skills
We are here to directly help you in understanding, practicing and implementing formulas of excel to make business reports and dashboards.
Nearly 20 domain’s real business use cases will be available for you to study and explore.
Familiarity with SQL is a must. Getting your hands more dirty in SQL will make your pockets more wealthy. If you are completely new to SQL, then it’s not a problem because we think SQL is one of the easiest languages. We will let you think, play and solve hundreds of our customized SQL exercises according to level of proficiency required.
Visualize and materialize. There is not a better way to analyze something and then present it into some beautiful, interactive visualization reports or dashboards. Tableau, Google Data Studio, Power BI these will be your friends in the process.
To give a smooth finishing touch or to build an entire platform of any data science project, python is a needed arsenal. Our team has some of the best python programmers which can help you throw away fear of python coding and make you learn some interesting python skills.
ANCOVA: Analysis of Covariance with python
ANCOVA is an extension of ANOVA (Analysis of Variance) that combines blocks of regression analysis and ANOVA. Which makes it Analysis of Covariance.
Learn Python The Fun Way
What if we learn topics in a desirable way!! What if we learn to write Python codes from gamers data !!
Meet the most efficient and intelligent AI assistant : NotebookLM
Start using NotebookLM today and embark on a smarter, more efficient learning journey!
Break the ice
This can be a super guide for you to start and excel in your data science career.
Tourism Trend Prediction
After tourism was established as a motivator of local economies (country, state), many governments stepped up to the plate.
Sentiment Analysis Polarity Detection using pos tag
Sentiment analysis can determine the polarity of sentiments from given sentences. We can classify them into certain categories.
For loop with Dictionary
Traverse a dictionary with for loop Accessing keys and values in dictionary. Use Dict.values() and Dict.keys() to generate keys and values as iterable. Nested Dictionaries with for loop Access Nested values of Nested Dictionaries How useful was this post? Click on a star to rate it! Submit Rating
For Loops with python
For loop is one of the most useful methods to reuse a code for repetitive execution.
Metrics and terminologies of digital analytics
These all metrics are revolving around visits and hits which we are getting on websites. Single page visits, Bounce, Cart Additions, Bounce Rate, Exit rate,
Hypothesis Testing
Hypothesis testing is a statistical method for determining whether or not a given hypothesis is true. A hypothesis can be any assumption based on data.
A/B testing
A/B tests are randomly controlled experiments. In A/B testing, you get user response on various versions of the product, and users are split within multiple versions of the product to figure out the “winner” of the version.
For Loop With Tuples
This article covers ‘for’ loops and how they are used with tuples. Even if the tuples are immutable, the accessibility of the tuples is similar to that of the list.
Multivariate ANOVA (MANOVA) with python
MANOVA is an update of ANOVA, where we use a minimum of two dependent variables.
Two-Way ANOVA
You only need to understand two or three concepts if you have read the one-way ANOVA article. We use two factors instead of one in a two-way ANOVA.
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